Hyper Elliptic Curve Encryption and Cost Minimization Approach in Moving Big Data to Cloud


 Sharlene McCoy
 1 years ago
 Views:
Transcription
1 554 Hyper Elliptic Curve Encryption and Cost Minimization Approach in Moving Big Data to Cloud Jerin Jose 1, Dr. Shine N Das 2 1 (Department of CSE, College of Engineering, Munnar) 2 (Department CSE, College of Engineering, Munnar) ABSTRACT Cloud computing is a latest computational system which can be used for big data processing. Huge amount of unstructured, structured and semi structured data can be called as big data. MapReduce and the Hadoop facilitate an affordable mechanism to handle and process data from multiple sources and store the big data in distributed cloud. This paper explains the secured and cost minimizing approach to move and store very large amount of data to cloud. Hyper elliptic cryptography is introduced in this paper to provide encryption to the huge amount of data arriving to the cloud. In addition to cryptography, data download module is included. So the paper mainly covers cost minimization in moving big data and the security of the big data. Keywords Big Data, Cloud Computing, Hyper Elliptic Curve Cryptography, Online Algorithm 1. INTRODUCTION Cloud computing is simply a service over the internet to store gigantic amount of data that our computers or single server cannot hold and facilitate services of computer over the Internet. That is it provides server resources such as storage, bandwidth and CPU to users. Its desirable feature is on demand supply of server resources and minimized management effort. Cloud platform is a collection/group of software and internet infrastructure integrated and hardware that are inter connected. The software  hardware services of cloud computing are available to enterprises, corporations, businesses markets and public. Essential characteristics of cloud computing are on demand selfservice, rapid elasticity, broad network access, resources pooling and measured service. Massive Scale, Geographic Distribution, Homogeneity, Virtualization, Low Cost Software, Resilient computing are some of the common features of cloud computing. Big data analysts concentrated their work more in the analyzing and processing of big data. Before analyzing, it is necessary to store the data in a storage area. As we know, the big data is intensively larger in volume, so the best way is to store it in the cloud. So we have to move the massive amount of data from the sources to the cloud. The big data should be moved to the cloud in a cost optimization manner and also it should be secure. Some works are done for moving big data to cloud by considering the cost minimization. But the data should be secured. So a security system is mandatory. So I implemented hyper elliptic curve cryptography which facilitates encryption to the arrived data in the cloud. 2. RELATED WORKS A series of recent work studies application migration to the cloud. The following are some of the related works on cloud computing and big data. Big Data is not just Hadoop [1]. This paper summarizes Hadoop as a costefficient platform and it has the ability to significantly lower the cost of certain workloads. Organizations may have particular pain around reducing the overall cost of their data warehouse. Certain groups of data may be seldom used and possible candidates to offload to a lowercost platform. Certain operations such as transformations may be offloaded to a more cost efficient platform. The primary area of value creation is cost savings. By pushing workloads and data sets onto a Hadoop platform, organizations are able to preserve their queries and take advantage of Hadoop s costeffective processing capabilities. One customer example, a financial services firm, moved processing of applications and reports from an operational data warehouse to Hadoop Hbase; they were able to preserve their existing queries and reduce the operating cost of their data management platform. A tunable workflow scheduling algorithm based on particle swarm optimization for cloud computing [2] explains that Cloud computing provides a pool of virtualized computing resources and adopts payperuse model. Schedulers for cloud computing make decision on how to allocate tasks of workflow to those virtualized computing resources. In this paper, a flexible particle swarm optimization (PSO) based scheduling algorithm to minimize both total cost and make span is presented. Experiment is conducted by varying computation of
2 555 tasks, number of particles and weight values of cost and makes span in fitness function. The results show that the proposed algorithm achieves both low cost and make span. In addition, it is adjustable according to different QoS constraints. PrivacyAware Cloud Deployment Scenario Selection [6] presented a privacyaware decision method for cloud deployment scenarios. This method is built upon the ProPAn and PACTS method. The first step of the presented method is the definition of the clouds used in concrete deployment scenarios and their cloud stakeholders. Then which domains shall be put into which defined clouds have to be decided. Then the defined clouds, cloud stakeholders, and the relation between existing domains and the defined clouds in domain knowledge diagrams have to be captured. We can apply ProPAn s graph generation algorithms on these domain knowledge diagrams together with a given model of the functional requirements in problem frames notation. The resulting privacy threat graphs are then analyzed to decide which deployment scenario best fits the privacy needs in the last step of the method. To support the method, they extended the ProPAntool with wizards that guide the user through the definition of the deployment scenarios and that automatically generate the corresponding domain knowledge diagrams. The proposed method scales well due to the modular way in that the relevant knowledge for the cloud deployment scenarios are integrated into the requirements model and the provided toolsupport. New Algorithms for Planning Bulk Transfer via Internet and Shipping Networks [9] is the first to explore the problem of planning a groupbased deadlineoriented data transfer in a scenario where data can be sent over both: (1) the internet, and (2) by shipping storage devices (e.g., external or hotplug drives, or SSDs) via companies such as Fedex, UPS, USPS, etc. The authors first formalize the problem and prove its NPHardness. Then, they propose novel algorithms and use them to build a planning system called Pandora (People and Networks Moving Data Around). Pandora uses new concepts of timeexpanded networks and deltatimeexpanded networks, combining them with integer programming techniques and optimizations for both shipping and internet edges. The experimental evaluation using real data from Fedex and from PlanetLab indicate the Pandora planner manages to satisfy deadlines and reduce costs significantly. Budgetconstrained bulk data transfer via internet and shipping networks [10] formulated and solved the problem of finding the fastest bulk data transfer plan given a strict budget constraint. The authors first characterized the solution space, and observed that the optimal solution can be found by searching through solutions to the deadlineconstrained minimum cost problem. Based on these observations, they devised a twostep binary search method that will find an optimal solution. They then developed a bounded binary search method that makes use of bounding functions that provide upper and lower bounds. In this paper the authors also presented two instances of bounding functions, based on variants of our data transfer networks, and proved that they do indeed provide bounds. Finally, they evaluated the proposed algorithms by running them on realistic network and found that the proposed techniques significantly reduce the time needed to compute solutions. Scaling social media applications into geodistributed clouds [8]. The paper exploits the social influences among users proposes efficient proactive algorithms for dynamic, optimal scaling of a social media application in a geodistributed cloud. The key contribution of this paper is an online content migration and request distribution algorithm with the following features: (1) future demand prediction by novelly characterizing social influences among the users in a simple but effective epidemic model; (2) oneshot optimal content migration and request distribution based on efficient optimization algorithms to address the predicted demand, and (3) a (t)step lookahead mechanism to adjust the oneshot optimization results towards the offline optimum. This paper also verifies the effectiveness of our algorithm using solid theoretical analysis, as well as largescale experiments under dynamic realistic settings on a homebuilt cloud platform. 3. METHODOLOGY 3.1 PROBLEM DEFINITION This work is focused on providing security in big data in cloud which arrives from data centers. Current approaches concentrate in big data analysis, and constraints regarding moving big data to cloud system. The proposed method is focused on encryption of data in cloud, downloading of data from cloud. The encryption method proposed here is Hyper Elliptic Curve Cryptography. The downloading module includes a clustering system to simplify the bottlenecks in downloading. 3.2 SYSTEM DESIGN We consider a cloud consisting of K geodistributed data centers in a set of regions K, where K = K. A cloud user (e.g., a global astronomical telescope application) continuously produces large volumes of data at a set D
3 556 of multiple geographic locations. The user connects to the data centers from different data generation locations via virtual private networks (VPNs), with G VPN gateways at the user side and K VPN gateways each collocated with a data center. Let the set of VPN gateways at the user side are denoted by G, with G = G. An illustration of the system is in Fig. 1. A private (the user s) network interrelates the data generation locations and the VPN gateways at the user side. Such a model demonstrates characteristic connection approaches between users and public clouds where devoted, private network connections are established between a user s premise and the cloud, for enhanced reliability and security, and guaranteed interconnection bandwidth. Interdata centre connections within a cloud are usually dedicated highbandwidth lines. Within the user s private network, the data transmission bandwidth between a data generation location d D and a VPN gateway g G is large as well. The bandwidth Ugi on a VPN link (g, i) from user side gateway g to data center i is restricted, and comprises the bottleneck in the system. Fig. 1 Block diagram of Feature based Sentiment Analysis Model PROBLEM FORMULATION Assume the system executes in a timeslotted fashion with slot length τ. F d (t) bytes of data are produced at location d in slot t, for uploading to the cloud. l dg is the latency between data location d D and user side gateway g G, p gi is the delay along VPN link (g, i), and ηik is the latency between data centers i and k. These delays, which can be obtained by a simple command such as ping, are dictated by the respective geographic distances. A cloud user needs to decide (i) via which VPN connections to upload its data to the cloud, and (ii) to which data center to aggregate data, for processing by a Map Reducelike framework, such that the monetary charges induced, as well as the latency for the data to reach the aggregation point, are jointly minimized. The total cost C to be minimized has four components: routing cost, migration cost, bandwidth cost and aggregate storage and computing cost OFFLINE ALGORITHM We propose a polynomialtime dynamic programming based algorithm to solve the offline optimal data migration problem, given absolute knowledge of data generation in the temporal domain. The derived offline optimal strategies serve as a benchmark for our online algorithms. The offline algorithm derives the theoretical minimum cost given complete knowledge of data generation in both temporal and spatial domains ONLINE ALGORITHM A straightforward algorithm solves the above optimization in each time slot, based on y(t 1) in the previous time slot. This can be far from optimal due to premature data migration. For example, assume data center k was selected at t 1, and migrating data from k to j is costoptimal at t according to the oneshot optimization (e.g., because more data are generated in region j in t); the offline optimum may indicate to keep all data in k at t, if the volume of data originated in k in t + 1 surges. We next explore dependencies among the selection of the aggregation data center across consecutive time slots, and design a more judicious online algorithm accordingly. We divide the overall cost C(x(t), y(t)) incurred in t into two parts: (i) migration cost Ct MG(y(t), y(t 1)) related to decisions in t 1; (ii) nonmigration cost that relies only on current information at t: Ct MG(x(t), y(t)) = CBW(x(t)) + CDC(y(t)) + CRT (x(t)). (1) We design an online algorithm, whose basic idea is to postpone data center switching even if the oneshot optimum indicates so, until the cumulative nonmigration cost (in Ct MG(x(t), y(t))) has significantly exceeded the potential data migration cost. At the beginning (t=1), we solve the oneshot optimization and upload data via the derived optimal routes x(1) to the optimal aggregation data center indicted by y(1). Let ˆt be the time of the data center switch. In each following time slot t, we compute the overall nonmigration cost in [ˆt, t 1], t 1 ν=ˆt Cν MG(x(ν), y(ν)). The algorithm checks whether this cost is at least β2 times the migration cost Cˆt MG(y(ˆt), y(ˆt 1)). If so, it solves the oneshot optimization to derive x(t) and y(t) without considering the migration cost, i.e., by minimizing Ct MG(x(t), y(t)) and an additional constraint, that the potential migration cost, Ct MG(y(t), y(t 1)), is no larger than β1 times the non migration cost Ct MG(x(t),
4 557 y(t)) at time t (to make sure that the migration cost is not too excessive). If a change of migration data center is indicated (y(t) = y(t 1)), the algorithm accepts the new aggregation decision, and migrates data accordingly. In all other cases, the aggregation data center remains unchanged from t 1, while optimal data routing paths are computed given this aggregation decision, for upload of new data generated in t. The Online Algorithm: 1: t = 1; 2: ˆt = 1; //Time slot when the last change of aggregation data center happens 3: Compute data routing decision x(1) and aggregation decision_y(1) by minimizing C(x(1), y(1)); 4: Compute C1 MG(y(1), y(0)) and C1 MG(x(1),y(1)); 5: while t T do 6: if Cˆt MG(y(ˆt), y(ˆt 1)) 1 β2 t 1 ν=ˆt Cν MG(x(ν), y(ν)) then 7: Derive x(t) and y(t) by minimizing Ct MG(x(t), y(t)) and constraint Ct MG(y(t), y(t 1)) β1ct MG(x(t), y(t)); 8: if y(t) = y(t 1) then 9: Use the new aggregation data center indicated by y(t); 10: ˆt = t; 11: if ˆt < t then //not to use new aggregation data center 12: y(t) = y(t 1), compute data routing decision x(t) if not derived; 13: t = t + 1; HYPER ELLIPTIC CURVES A hyper elliptic curve C of genus g outlined over a field Fq of characteristic p is given by associate degree equation of the form y 2 + h(x)y = f(x) where h(x) and f(x) square measure polynomials with coefficients in Fq, with deg h(x) g and deg f(x) = 2g + one. an extra demand is that C isn't a singular curve. If h(x) = zero and p > a pair of this amounts to the necessity that f(x) could be a square free polynomial. In general, the condition is that there aren't any x and y in the pure mathematics closure of Fq that satisfy the equation (1) and also the 2 partial derivatives 2y + h(x) = zero and h (x)y f (x) = SCHEMES Signature schemes, encryption schemes and key agreement schemes are the schemes which can base on elliptic and hyper elliptic curves. DiffieHellman Key Agreement Scheme: Two parties Sender and Receiver wish to agree on a common secret by communicating over a public channel. An eavesdropper Interrupter, who can listen to all communication between Sender and Receiver, should not be able to obtain this common secret. First, we assume that there are the following publicly known system parameters: The group G. An element R G of large prime order r. The steps that Sender performs are the following: 1. Choose a random integer a [1, r 1]. 2. Compute P ar in the group G, and send it to Receiver. 3. Receive the element Q G from Receiver. 4. Compute S = aq as common secret. The steps that Receiver performs are: 1. Choose a random integer b [1, r 1]. 2. Compute Q = br in the group G, and send it to Sender. 3. Receive the element P G from Sender. 4. Compute S = bp as common secret. Note that both Sender and Receiver have computed the same values S, as S = a(br) = (ab)r = b(ar). It is not known how Interrupter, knowing only P, Q and R, can compute S within reasonable time. If she could solve the discrete logarithm problem in G, then she could calculate a from P and R, and then calculate S = aq. The problem of computing S from P, Q and R is known as the DiffieHellman problem. The pair (a, P) is called Sender s key pair consisting of her private key and public key P. Likewise, Receiver s key pair is (b, Q), with private key b and public key Q. It is important to realize that the scheme that is described here should be used with additional forms of authentication of the public keys. Otherwise an auditor (interrupter) who is able to intercept and change information sent and is able to agree on keys separately with Sender and Receiver. This is known as a man in the middle attack. The (Hyper) Elliptic Curve Integrated Encryption Scheme: This encryption scheme uses the Diffie Hellman scheme to derive a secret key, and combines it with tools from symmetric key cryptography to provide better provable security. It can be proved to be securing against adaptive chosen cipher text attacks. We again formulate the scheme for any group G and R G with large prime order r. The symmetric tools that are used in the scheme are: A key derivation function. This is a function KD(P) that takes as input a key P, in our case this is an element of G, and outputs keying data of any required length. A symmetric encryption scheme consisting of a function Enc k that encrypts the message M to a ciphertext C = Enc k (M) using a key k, and a function Deck that decrypts C to the message M = Deck(C).
5 558 A Message Authentication Code MAC k. One can think of this as a keyed hash function. It is a function that takes as input a ciphertext C and a key k. It computes a string MAC k (C) that satisfies the following property: Given a number of pairs (Ci, MAC k (Ci)), it is computationally infeasible to determine a pair (C, MACk (C)), with C different from the Ci if one does not know k. 1. Obtain Receiver s public key Q. 2. Choose a secret number a [1, r 1]. 3. Compute C1 = ar. 4. Compute C2 = aq. 5. Compute two keys k1 and k2 from KD(C2), i.e. (k1 k2) = KD(C2). 6. Encrypt the message, C = Enck1 (M). 7. Compute mac = MACK2 (C). 8. Send (C1, C, mac). To decrypt, Receiver does the following: 1. Obtain the encrypted message (C1, C, mac) from Sender. 2. Compute C2 = bc1. 3. Compute the keys k1 and k2 from KD(C2). 4. Check whether mac equals MACk2 (C). If not, reject the message and stop. 5. Decrypt the message M = Deck1 (C). The Digital Signature Algorithm (DSA) is the basis of the digital NIST signature standard. This algorithm can be adapted for elliptic and hyper elliptic curves. More generally, one can use it for any group G where the DLP is difficult, provided that one has a computable map G Z with large enough image and few inverses for each element in the image. The elliptic curve version, known as ECDSA, can be found in various standards. The hyper elliptic curve version seems not to have appeared a lot in existing literature. In the hyper elliptic curve case, one can take for φ the following map. Let D = [u(x), v(x)] be a divisor in Mumford representation.. Let u(x) = deg(u(x)) u i x i with ui Fq. Define φ(d) to be the integer whose binary expansion is the concatenation of the bit strings representing the ui, i [0, deg(u(x)) 1], as explained above. Assume the following system parameters are publicly known: A group G and a map φ Z as above, An element R G with large prime order r, A hash function H that maps messages m to 160bit integers. To create a key pair, Alice chooses a secret integer a Z, and computes P = ar. The number a is Alice s secret key, and P is her public key. If Alice wants to sign a message m, she has to do the following: 1. Choose a random integer k [1, r 1], and compute Q = kr. 2. Compute s k 1 (H(m) + aφ(q)) mod r. The signature is (m, Q, s). To verify this signature, a verifier Bob has to do the following: 1. Compute v1 s 1H(m) mod r and v2 s 1φ(Q) mod r. 2. Compute V = v1r + v2p. 3. Accept the signature if V = Q. Otherwise, reject it. The hyper elliptic curve as explained above is implemented at the cloud side before storing it to the cloud. It makes more protection to the stored data. The data arrived at the cloud are divided into chunks and the chunks are pass through the hyper elliptic curve encryption system. Then it is converted into encrypted form. These encrypted files are stored in the cloud. 4. RESULTS We compare the performance of our scheme with the previous paper. The previous paper didn t use any security measures for storing the big data in cloud. This paper employed encryption for the big data which gives more advantage and efficiency to the system. The computation and communication overhead when we used the encryption to entire file (n) and randomly choose file(c) is shown in the TABLE 1. It is much lesser but provides great achievement to the work. Table 1. Comparison of Overheads n = 100,000 c = 460 Computation Overhead sec 0.21 sec Communication 2.11 MB KB Overhead Signature generation time, extra storage space on signatures are also evaluated with some other previous works which uses another encryption method and the result is obtained as shown in the TABLE 2. Table 2. Comparison of Signature Complexity [12] [13] Signature Generation Time (ms) Extra storage space on signatures (MB) CONCLUSION In this paper, we used an efficient security system for the big data in the cloud. So the data in the cloud kept safely. The encryption method used is the Hyper Elliptic Curve Cryptosystem with use the mathematical concepts of Hyper Elliptic Curve to encrypt the data. This work is
6 559 done with the help of Cent OS, Horton works Sandbox. The vibrant features of Java can be used for making the theory into reality. This paper also considered the download of data from cloud after clustering it. REFERENCES [1] M. Armbrust, A. Fox, R. Grifth, A. D. Joseph, R. Katz, A. Konwinski, G. Lee, D. P. A. Rabkin, I. Stoica, and M. Zaharia, Above the Clouds: A Berkeley View of Cloud Computing, EECS, University of California, Berkeley, Tech. Rep., [2] S. Pandey, L. Wu, S. Guru, and R. Buyya, A Particle Swarm Optimization (PSO)based Heuristic for Scheduling Workflow Applications in Cloud Computing Environment, in Proc. of IEEE AINA, [3] E. E. Schadt, M. D. Linderman, J. Sorenson, L. Lee, and G. P. Nolan, Computational Solutions to Largescale Data Management and Analysis, Nat Rev Genet, vol. 11, no. 9, pp , [4] R. J. Brunner, S. G. Djorgovski, T. A. Prince, and A. S. Szalay, Handbook of Massive Data Sets, J. Abello, P. M. Pardalos, and M. G. C. Resende, Eds. Norwell, MA, USA: Kluwer Academic Publishers, 2002, ch. Massive Datasets in Astronomy, pp [5] M. Cardosa, C. Wang, A. Nangia, A. Chandra, and J. Weissman, Exploring MapReduce Efficiency with HighlyDitributed Data, in Proc. of ACM MapReduce, [6] M. Hajjat, X. Sun, Y. E. Sung, D. Maltz, and S. Rao, Cloudward Bound: Planning for Beneficial Migration of Enterprise Applications to the Cloud, in Proc. of ACM SIGCOM, August [7] X. Cheng and J. Liu, LoadBalanced Migration of Social Media to Content Clouds, in Proc. of ACM NOSSDAV, June [8] Y. Wu, C. Wu, B. Li, L. Zhang, Z. Li, and F. Lau, Scaling Social Media Applications into Geo Distributed Clouds, in Proc. of IEEE INFOCOM, Mar [9] B. Cho and I. Gupta, New Algorithms for Planning Bulk Transfer via Internet and Shipping Networks, in Proc. of IEEE ICDCS, [10] B. Cho and I. Gupta, BudgetConstrained Bulk Data Transfer via Internet and Shipping Networks, in Proc. of ACM ICAC, [11] J. Scholten and F. Vercauteren, An Introduction to Elliptic and Hyperelliptic Curve Cryptography and the NTRU Cryptosystem. [12] B. Wang, B. Li, and H. Li, Oruta: Privacy Preserving Public Auditing for Shared Data in the Cloud, in IEEE Cloud, June 2012, pp [13] B. Wang, B. Li, and H. Li, Knox: Privacy Preserving Auditing for Shared Data with Large Groups in the Cloud, in ACNS, 2012, pp
Big Data is Not just Hadoop
Asiapacific Journal of Multimedia Services Convergence with Art, Humanities and Sociology Vol.2, No.1 (2012), pp. 1318 http://dx.doi.org/10.14257/ajmscahs.2012.06.04 Big Data is Not just Hadoop Ronnie
More informationMoving Big Data to The Cloud
Moving Big Data to The Cloud Linquan Zhang, Chuan Wu, Zongpeng Li, Chuanxiong Guo, Minghua Chen and Francis C.M. Lau The University of Hong Kong, Hong Kong University of Calgary, Canada Microsoft Research
More informationDevelopment of enhanced Third party Auditing Scheme for Secure Cloud Storage
Development of enhanced Third party Auditing Scheme for Secure Cloud Storage Bhanu Prakash Chamakuri*1, D. Srikar*2, Dr. M.Suresh Babu*3 M.Tech Scholar, Dept of CSE, Grandhi Varalakshmi Institute Of Technology,
More informationSECURITY ENHANCEMENT OF GROUP SHARING AND PUBLIC AUDITING FOR DATA STORAGE IN CLOUD
SECURITY ENHANCEMENT OF GROUP SHARING AND PUBLIC AUDITING FOR DATA STORAGE IN CLOUD S.REVATHI B.HASEENA M.NOORUL IZZATH PG Student PG Student PG Student II ME CSE II ME CSE II ME CSE AlAmeen Engineering
More informationData Integrity for Secure Dynamic Cloud Storage System Using TPA
International Journal of Electronic and Electrical Engineering. ISSN 09742174, Volume 7, Number 1 (2014), pp. 712 International Research Publication House http://www.irphouse.com Data Integrity for Secure
More information1720  Forward Secrecy: How to Secure SSL from Attacks by Government Agencies
1720  Forward Secrecy: How to Secure SSL from Attacks by Government Agencies Dave Corbett Technical Product Manager Implementing Forward Secrecy 1 Agenda Part 1: Introduction Why is Forward Secrecy important?
More informationSecure Network Communication Part II II Public Key Cryptography. Public Key Cryptography
Kommunikationssysteme (KSy)  Block 8 Secure Network Communication Part II II Public Key Cryptography Dr. Andreas Steffen 20002001 A. Steffen, 28.03.2001, KSy_RSA.ppt 1 Secure Key Distribution Problem
More informationKeywords Cloud computing, Encryption, Data integrity, Third Party Auditor (TPA), RC5 Algorithm, privacypreserving,
Volume 3, Issue 11, November 2013 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Third Party
More informationPublic Key Cryptography. c Eli Biham  March 30, 2011 258 Public Key Cryptography
Public Key Cryptography c Eli Biham  March 30, 2011 258 Public Key Cryptography Key Exchange All the ciphers mentioned previously require keys known apriori to all the users, before they can encrypt
More informationEFFICIENT AND SECURE DATA PRESERVING IN CLOUD USING ENHANCED SECURITY
EFFICIENT AND SECURE DATA PRESERVING IN CLOUD USING ENHANCED SECURITY Siliveru Ashok kumar* S.G. Nawaz ## and M.Harathi # * Student of M.Tech, Sri Krishna Devaraya Engineering College, Gooty # Department
More informationAN EFFICIENT STRATEGY OF THE DATA INTEGRATION BASED CLOUD
INTERNATIONAL JOURNAL OF REVIEWS ON RECENT ELECTRONICS AND COMPUTER SCIENCE AN EFFICIENT STRATEGY OF THE DATA INTEGRATION BASED CLOUD Koncha Anantha Laxmi Prasad 1, M.Yaseen Pasha 2, V.Hari Prasad 3 1
More informationIndex Terms: Cloud Computing, Third Party Auditor, Threats In Cloud Computing, Dynamic Encryption.
Secure PrivacyPreserving Cloud Services. Abhaya Ghatkar, Reena Jadhav, Renju Georgekutty, Avriel William, Amita Jajoo DYPCOE, Akurdi, Pune ghatkar.abhaya@gmail.com, jadhavreena70@yahoo.com, renjug03@gmail.com,
More informationData Integrity Check using Hash Functions in Cloud environment
Data Integrity Check using Hash Functions in Cloud environment Selman Haxhijaha 1, Gazmend Bajrami 1, Fisnik Prekazi 1 1 Faculty of Computer Science and Engineering, University for Business and Tecnology
More informationNEW DIGITAL SIGNATURE PROTOCOL BASED ON ELLIPTIC CURVES
NEW DIGITAL SIGNATURE PROTOCOL BASED ON ELLIPTIC CURVES Ounasser Abid 1, Jaouad Ettanfouhi 2 and Omar Khadir 3 1,2,3 Laboratory of Mathematics, Cryptography and Mechanics, Department of Mathematics, Fstm,
More informationOutline. Computer Science 418. Digital Signatures: Observations. Digital Signatures: Definition. Definition 1 (Digital signature) Digital Signatures
Outline Computer Science 418 Digital Signatures Mike Jacobson Department of Computer Science University of Calgary Week 12 1 Digital Signatures 2 Signatures via Public Key Cryptosystems 3 Provable 4 Mike
More informationA ProxyBased Data Security Solution in Mobile Cloud
, pp. 7784 http://dx.doi.org/10.14257/ijsia.2015.9.5.08 A ProxyBased Data Security Solution in Mobile Cloud Xiaojun Yu 1,2 and Qiaoyan Wen 1 1 State Key Laboratory of Networking and Switching Technology,
More informationComputer Security: Principles and Practice
Computer Security: Principles and Practice Chapter 20 PublicKey Cryptography and Message Authentication First Edition by William Stallings and Lawrie Brown Lecture slides by Lawrie Brown PublicKey Cryptography
More informationSoftware Implementation of GongHarn Publickey Cryptosystem and Analysis
Software Implementation of GongHarn Publickey Cryptosystem and Analysis by Susana Sin A thesis presented to the University of Waterloo in fulfilment of the thesis requirement for the degree of Master
More informationEnable Public Audit ability for Secure Cloud Storage
Enable Public Audit ability for Secure Cloud Storage Leela Poornima 1, D.Hari Krishna 2 1 Student, Nova College of Engineering and Technology, Ibrahimpatnam,Krishna Dist., Andhra Pradesh, India 2 Assistant
More informationAn Efficient data storage security algorithm using RSA Algorithm
An Efficient data storage security algorithm using RSA Algorithm Amandeep Kaur 1, Sarpreet Singh 2 1 Research fellow, Department of Computer Science and Engineering, Sri Guru Granth Sahib World University,
More informationSECURITY IMPROVMENTS TO THE DIFFIEHELLMAN SCHEMES
www.arpapress.com/volumes/vol8issue1/ijrras_8_1_10.pdf SECURITY IMPROVMENTS TO THE DIFFIEHELLMAN SCHEMES Malek Jakob Kakish Amman Arab University, Department of Computer Information Systems, P.O.Box 2234,
More informationA Factoring and Discrete Logarithm based Cryptosystem
Int. J. Contemp. Math. Sciences, Vol. 8, 2013, no. 11, 511517 HIKARI Ltd, www.mhikari.com A Factoring and Discrete Logarithm based Cryptosystem Abdoul Aziz Ciss and Ahmed Youssef Ecole doctorale de Mathematiques
More informationPrivacy Preserving Distributed Cloud Storage
Privacy Preserving Distributed Cloud Storage Praveenkumar Khethavath 1 *, Doyel Pal 2 1 Department of Mathematics, Engineering and Computer Science, LaGuardia Community College, Long Island City, NY 11101.
More informationInternational Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, MayJun 2014
RESEARCH ARTICLE OPEN ACCESS Survey of Optimization of Scheduling in Cloud Computing Environment Er.Mandeep kaur 1, Er.Rajinder kaur 2, Er.Sughandha Sharma 3 Research Scholar 1 & 2 Department of Computer
More informationPublic Key Cryptography. Performance Comparison and Benchmarking
Public Key Cryptography Performance Comparison and Benchmarking Tanja Lange Department of Mathematics Technical University of Denmark tanja@hyperelliptic.org 28.08.2006 Tanja Lange Benchmarking p. 1 What
More informationCLOUD COMPUTING SECURITY IN UNRELIABLE CLOUDS USING RELIABLE REENCRYPTION
CLOUD COMPUTING SECURITY IN UNRELIABLE CLOUDS USING RELIABLE REENCRYPTION Chandrala DN 1, Kulkarni Varsha 2 1 Chandrala DN, M.tech IV sem,department of CS&E, SVCE, Bangalore 2 Kulkarni Varsha, Asst. Prof.
More informationIMPLEMENTATION CONCEPT FOR ADVANCED CLIENT REPUDIATION DIVERGE AUDITOR IN PUBLIC CLOUD
IMPLEMENTATION CONCEPT FOR ADVANCED CLIENT REPUDIATION DIVERGE AUDITOR IN PUBLIC CLOUD 1 Ms.Nita R. Mhaske, 2 Prof. S.M.Rokade 1 student, Master of Engineering, Dept. of Computer Engineering Sir Visvesvaraya
More informationPublic Key Cryptography and RSA. Review: Number Theory Basics
Public Key Cryptography and RSA Murat Kantarcioglu Based on Prof. Ninghui Li s Slides Review: Number Theory Basics Definition An integer n > 1 is called a prime number if its positive divisors are 1 and
More informationA New Efficient Digital Signature Scheme Algorithm based on Block cipher
IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 22780661, ISBN: 22788727Volume 7, Issue 1 (Nov.  Dec. 2012), PP 4752 A New Efficient Digital Signature Scheme Algorithm based on Block cipher 1
More informationPublic Key (asymmetric) Cryptography
PublicKey Cryptography UNIVERSITA DEGLI STUDI DI PARMA Dipartimento di Ingegneria dell Informazione Public Key (asymmetric) Cryptography Luca Veltri (mail.to: luca.veltri@unipr.it) Course of Network Security,
More informationA Comparative Study of Applying Real Time Encryption in Cloud Computing Environments
A Comparative Study of Applying Real Time Encryption in Cloud Computing Environments Faraz Fatemi Moghaddam (f.fatemi@ieee.org) Omidreza Karimi (omid@medicatak.com.my) Dr. Ma en T. Alrashdan (dr.maen@apu.edu.my)
More informationCapture Resilient ElGamal Signature Protocols
Capture Resilient ElGamal Signature Protocols Hüseyin Acan 1, Kamer Kaya 2,, and Ali Aydın Selçuk 2 1 Bilkent University, Department of Mathematics acan@fen.bilkent.edu.tr 2 Bilkent University, Department
More informationPublic Auditing for Shared Data in the Cloud by Using AES
Public Auditing for Shared Data in the Cloud by Using AES 1 Syagamreddy Subbareddy, 2 P.Tejaswi, 3 D.Krishna 1 M.Tech(CSE) Pursuing, 2 Associate Professor, 3 Associate Professor,HOD, 1,2,3 Dept. of Computer
More informationCIS 6930 Emerging Topics in Network Security. Topic 2. Network Security Primitives
CIS 6930 Emerging Topics in Network Security Topic 2. Network Security Primitives 1 Outline Absolute basics Encryption/Decryption; Digital signatures; DH key exchange; Hash functions; Application of hash
More informationPublic Key Cryptography Overview
Ch.20 PublicKey Cryptography and Message Authentication I will talk about it later in this class Final: Wen (5/13) 16301830 HOLM 248» give you a sample exam» Mostly similar to homeworks» no electronic
More informationSignature Amortization Technique for Authenticating Delay Sensitive Stream
Signature Amortization Technique for Authenticating Delay Sensitive Stream M Bruntha 1, Dr J. Premalatha Ph.D. 2 1 M.E., 2 Professor, Department of Information Technology, Kongu Engineering College, Perundurai,
More informationLecture 9: Application of Cryptography
Lecture topics Cryptography basics Using SSL to secure communication links in J2EE programs Programmatic use of cryptography in Java Cryptography basics Encryption Transformation of data into a form that
More informationModule 8. Network Security. Version 2 CSE IIT, Kharagpur
Module 8 Network Security Lesson 2 Secured Communication Specific Instructional Objectives On completion of this lesson, the student will be able to: State various services needed for secured communication
More informationCloud Data Storage Services Considering Public Audit for Security
Global Journal of Computer Science and Technology Cloud and Distributed Volume 13 Issue 1 Version 1.0 Year 2013 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals
More informationData Security in Cloud Using Elliptic Curve Crytography
Data Security in Cloud Using Elliptic Curve Crytography Puneetha C 1, Dr. M Dakshayini 2 PG Student, Dept. of Information Science & Engineering, B.M.S.C.E, Karnataka, Bangalore,India 1 Professor, Dept.
More informationSharing Of Multi Owner Data in Dynamic Groups Securely In Cloud Environment
Sharing Of Multi Owner Data in Dynamic Groups Securely In Cloud Environment Deepa Noorandevarmath 1, Rameshkumar H.K 2, C M Parameshwarappa 3 1 PG Student, Dept of CS&E, STJIT, Ranebennur. Karnataka, India
More informationSecuring MANET Using Diffie Hellman Digital Signature Scheme
Securing MANET Using Diffie Hellman Digital Signature Scheme Karamvir Singh 1, Harmanjot Singh 2 1 Research Scholar, ECE Department, Punjabi University, Patiala, Punjab, India 1 Karanvirk09@gmail.com 2
More informationOverview of PublicKey Cryptography
CS 361S Overview of PublicKey Cryptography Vitaly Shmatikov slide 1 Reading Assignment Kaufman 6.16 slide 2 PublicKey Cryptography public key public key? private key Alice Bob Given: Everybody knows
More informationKeywords Cloud Storage, Error Identification, Partitioning, Cloud Storage Integrity Checking, Digital Signature Extraction, Encryption, Decryption
Partitioning Data and Domain Integrity Checking for Storage  Improving Cloud Storage Security Using Data Partitioning Technique Santosh Jogade *, Ravi Sharma, Prof. Rajani Kadam Department Of Computer
More informationINTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY
INTERNATIONAL JOURNAL OF PURE AND APPLIED RESEARCH IN ENGINEERING AND TECHNOLOGY A PATH FOR HORIZING YOUR INNOVATIVE WORK IMPLEMENTATION OF AN APPROACH TO ENHANCE QOS AND QOE BY MIGRATING SERVICES IN CLOUD
More informationOverview of Cryptographic Tools for Data Security. Murat Kantarcioglu
UT DALLAS Erik Jonsson School of Engineering & Computer Science Overview of Cryptographic Tools for Data Security Murat Kantarcioglu Pag. 1 Purdue University Cryptographic Primitives We will discuss the
More informationAnalysis on Secure Data sharing using ELGamal s Cryptosystem in Cloud
Analysis on Secure Data sharing using ELGamal s Cryptosystem in Cloud M.Jayanthi, Assistant Professor, Hod of MCA.E mail: badini_jayanthi@yahoo.co.in MahatmaGandhi University,Nalgonda, INDIA. B.Ranganatha
More informationThe Mathematics of the RSA PublicKey Cryptosystem
The Mathematics of the RSA PublicKey Cryptosystem Burt Kaliski RSA Laboratories ABOUT THE AUTHOR: Dr Burt Kaliski is a computer scientist whose involvement with the security industry has been through
More informationSFWR ENG 4C03  Computer Networks & Computer Security
KEY MANAGEMENT SFWR ENG 4C03  Computer Networks & Computer Security Researcher: Jayesh Patel Student No. 9909040 Revised: April 4, 2005 Introduction Key management deals with the secure generation, distribution,
More informationA Novel Approach for Signing Multiple Messages: Hash Based Signature
International Journal of Information & Computation Technology. ISSN 09742239 Volume 4, Number 15 (2014), pp. International Research Publications House http://www. irphouse.com A Novel Approach for Signing
More informationFinal Exam. IT 4823 Information Security Administration. Rescheduling Final Exams. Kerberos. Idea. Ticket
IT 4823 Information Security Administration Public Key Encryption Revisited April 5 Notice: This session is being recorded. Lecture slides prepared by Dr Lawrie Brown for Computer Security: Principles
More informationA Hybrid Scheduling Approach for Scalable Heterogeneous Hadoop Systems
A Hybrid Scheduling Approach for Scalable Heterogeneous Hadoop Systems Aysan Rasooli Department of Computing and Software McMaster University Hamilton, Canada Email: rasooa@mcmaster.ca Douglas G. Down
More informationInternational Journal of Information Technology, Modeling and Computing (IJITMC) Vol.1, No.3,August 2013
FACTORING CRYPTOSYSTEM MODULI WHEN THE COFACTORS DIFFERENCE IS BOUNDED Omar Akchiche 1 and Omar Khadir 2 1,2 Laboratory of Mathematics, Cryptography and Mechanics, Fstm, University of Hassan II MohammediaCasablanca,
More informationIMPROVED SECURITY MEASURES FOR DATA IN KEY EXCHANGES IN CLOUD ENVIRONMENT
INTERNATIONAL JOURNAL OF RESEARCH IN COMPUTER APPLICATIONS AND ROBOTICS ISSN 23207345 IMPROVED SECURITY MEASURES FOR DATA IN KEY EXCHANGES IN CLOUD ENVIRONMENT Merlin Shirly T 1, Margret Johnson 2 1 PG
More informationImplementation of Elliptic Curve Digital Signature Algorithm
Implementation of Elliptic Curve Digital Signature Algorithm Aqeel Khalique Kuldip Singh Sandeep Sood Department of Electronics & Computer Engineering, Indian Institute of Technology Roorkee Roorkee, India
More informationCSCE 465 Computer & Network Security
CSCE 465 Computer & Network Security Instructor: Dr. Guofei Gu http://courses.cse.tamu.edu/guofei/csce465/ Public Key Cryptogrophy 1 Roadmap Introduction RSA DiffieHellman Key Exchange Public key and
More informationSecrecy Maintaining Public Inspecting For Secure Cloud Storage
Secrecy Maintaining Public Inspecting For Secure Cloud Storage K.Sangamithra 1, S.Tamilselvan 2 M.E, M.P.Nachimuthu.M.Jaganathan Engineering College, Tamilnadu, India 1 Asst. Professor, M.P.Nachimuthu.M.Jaganathan
More informationSECURE AND EFFICIENT PRIVACYPRESERVING PUBLIC AUDITING SCHEME FOR CLOUD STORAGE
International Journal of Computer Network and Security(IJCNS) Vol 7. No.1 2015 Pp. 18 gopalax Journals, Singapore available at : www.ijcns.com ISSN: 09758283 
More informationA Hybrid Load Balancing Policy underlying Cloud Computing Environment
A Hybrid Load Balancing Policy underlying Cloud Computing Environment S.C. WANG, S.C. TSENG, S.S. WANG*, K.Q. YAN* Chaoyang University of Technology 168, Jifeng E. Rd., Wufeng District, Taichung 41349
More informationDigital Signatures. Meka N.L.Sneha. Indiana State University. nmeka@sycamores.indstate.edu. October 2015
Digital Signatures Meka N.L.Sneha Indiana State University nmeka@sycamores.indstate.edu October 2015 1 Introduction Digital Signatures are the most trusted way to get documents signed online. A digital
More informationSecured Storage of Outsourced Data in Cloud Computing
Secured Storage of Outsourced Data in Cloud Computing Chiranjeevi Kasukurthy 1, Ch. Ramesh Kumar 2 1 M.Tech(CSE), Nalanda Institute of Engineering & Technology,Siddharth Nagar, Sattenapalli, Guntur Affiliated
More informationCryptography and Network Security
Cryptography and Network Security Fifth Edition by William Stallings Chapter 9 Public Key Cryptography and RSA PrivateKey Cryptography traditional private/secret/single key cryptography uses one key shared
More informationClient Server Registration Protocol
Client Server Registration Protocol The ClientServer protocol involves these following steps: 1. Login 2. Discovery phase User (Alice or Bob) has K s Server (S) has hash[pw A ].The passwords hashes are
More informationNetwork Security. Computer Networking Lecture 08. March 19, 2012. HKU SPACE Community College. HKU SPACE CC CN Lecture 08 1/23
Network Security Computer Networking Lecture 08 HKU SPACE Community College March 19, 2012 HKU SPACE CC CN Lecture 08 1/23 Outline Introduction Cryptography Algorithms Secret Key Algorithm Message Digest
More informationNotes on Network Security Prof. Hemant K. Soni
Chapter 9 Public Key Cryptography and RSA PrivateKey Cryptography traditional private/secret/single key cryptography uses one key shared by both sender and receiver if this key is disclosed communications
More informationEfficient Framework for Deploying Information in Cloud Virtual Datacenters with Cryptography Algorithms
Efficient Framework for Deploying Information in Cloud Virtual Datacenters with Cryptography Algorithms Radhika G #1, K.V.V. Satyanarayana *2, Tejaswi A #3 1,2,3 Dept of CSE, K L University, Vaddeswaram522502,
More informationMonitoring Data Integrity while using TPA in Cloud Environment
Monitoring Data Integrity while using TPA in Cloud Environment Jaspreet Kaur, Jasmeet Singh Abstract Cloud Computing is the arising technology that delivers software, platform and infrastructure as a service
More informationNetwork Security [2] Plain text Encryption algorithm Public and private key pair Cipher text Decryption algorithm. See next slide
Network Security [2] Public Key Encryption Also used in message authentication & key distribution Based on mathematical algorithms, not only on operations over bit patterns (as conventional) => much overhead
More informationSECURITY FOR ENCRYPTED CLOUD DATA BY USING TOPKEY TREE TECHNOLOGIES
SECURITY FOR ENCRYPTED CLOUD DATA BY USING TOPKEY TREE TECHNOLOGIES 1 MANJOORULLASHA SHAIK, 2 SYED.ABDULHAQ, 3 P.BABU 1 PG SCHOLAR, CSE (CN), QCET, NELLORE 2,3 ASSOCIATE PROFESSOR, CSE, QCET, NELLORE
More informationSheltered MultiOwner Data distribution For vibrant Groups in the Cloud
Sheltered MultiOwner Data distribution For vibrant Groups in the Cloud I.sriram murthy 1 N.Jagajeevan 2 II MTech student Assistant.Professor Department of computer science & Engineering Department of
More informationNetwork Security. Security Attacks. Normal flow: Interruption: 孫 宏 民 hmsun@cs.nthu.edu.tw Phone: 035742968 國 立 清 華 大 學 資 訊 工 程 系 資 訊 安 全 實 驗 室
Network Security 孫 宏 民 hmsun@cs.nthu.edu.tw Phone: 035742968 國 立 清 華 大 學 資 訊 工 程 系 資 訊 安 全 實 驗 室 Security Attacks Normal flow: sender receiver Interruption: Information source Information destination
More informationDigital Signatures. (Note that authentication of sender is also achieved by MACs.) Scan your handwritten signature and append it to the document?
Cryptography Digital Signatures Professor: Marius Zimand Digital signatures are meant to realize authentication of the sender nonrepudiation (Note that authentication of sender is also achieved by MACs.)
More informationA COMPARATIVE STUDY OF SECURE SEARCH PROTOCOLS IN PAY ASYOUGO CLOUDS
A COMPARATIVE STUDY OF SECURE SEARCH PROTOCOLS IN PAY ASYOUGO CLOUDS V. Anand 1, Ahmed Abdul Moiz Qyser 2 1 Muffakham Jah College of Engineering and Technology, Hyderabad, India 2 Muffakham Jah College
More informationMINIMIZING STORAGE COST IN CLOUD COMPUTING ENVIRONMENT
MINIMIZING STORAGE COST IN CLOUD COMPUTING ENVIRONMENT 1 SARIKA K B, 2 S SUBASREE 1 Department of Computer Science, Nehru College of Engineering and Research Centre, Thrissur, Kerala 2 Professor and Head,
More informationThird Party Auditing For Secure Data Storage in Cloud through Trusted Third Party Auditor Using RC5
Third Party Auditing For Secure Data Storage in Cloud through Trusted Third Party Auditor Using RC5 Miss. Nupoor M. Yawale 1, Prof. V. B. Gadicha 2 1 Student, M.E. Second year CSE, P R Patil COET, Amravati.INDIA.
More informationSECURE REENCRYPTION IN UNRELIABLE CLOUD USINGSYNCHRONOUS CLOCK
International Journal of Advance Research In Science And Engineering IJARSE, Vol. No.4, Issue No.01, January 2015 http:// SECURE REENCRYPTION IN UNRELIABLE CLOUD USINGSYNCHRONOUS CLOCK Arudra Gopala Rao
More informationMoving Big Data to The Cloud: An Online CostMinimizing Approach
Moving Big Data to The Cloud: An Online CostMinimizing Approach Linquan Zhang, Chuan Wu, Zongpeng Li, Chuanxiong Guo, Minghua Chen and Francis C.M. Lau Abstract Cloud computing, rapidly emerging as a
More informationLukasz Pater CMMS Administrator and Developer
Lukasz Pater CMMS Administrator and Developer EDMS 1373428 Agenda Introduction Why do we need asymmetric ciphers? Oneway functions RSA Cipher Message Integrity Examples Secure Socket Layer Single Sign
More informationSecret Sharing based on XOR for Efficient Data Recovery in Cloud
Secret Sharing based on XOR for Efficient Data Recovery in Cloud Computing Environment SuHyun Kim, ImYeong Lee, First Author Division of Computer Software Engineering, Soonchunhyang University, kimsh@sch.ac.kr
More informationCURTAIL THE EXPENDITURE OF BIG DATA PROCESSING USING MIXED INTEGER NONLINEAR PROGRAMMING
Journal homepage: http://www.journalijar.com INTERNATIONAL JOURNAL OF ADVANCED RESEARCH RESEARCH ARTICLE CURTAIL THE EXPENDITURE OF BIG DATA PROCESSING USING MIXED INTEGER NONLINEAR PROGRAMMING R.Kohila
More informationDigital Signature. Raj Jain. Washington University in St. Louis
Digital Signature Raj Jain Washington University in Saint Louis Saint Louis, MO 63130 Jain@cse.wustl.edu Audio/Video recordings of this lecture are available at: http://www.cse.wustl.edu/~jain/cse57111/
More informationAn Approach to Shorten Digital Signature Length
Computer Science Journal of Moldova, vol.14, no.342, 2006 An Approach to Shorten Digital Signature Length Nikolay A. Moldovyan Abstract A new method is proposed to design short signature schemes based
More informationFirst Semester Examinations 2011/12 INTERNET PRINCIPLES
PAPER CODE NO. EXAMINER : Martin Gairing COMP211 DEPARTMENT : Computer Science Tel. No. 0151 795 4264 First Semester Examinations 2011/12 INTERNET PRINCIPLES TIME ALLOWED : Two Hours INSTRUCTIONS TO CANDIDATES
More informationCRYPTOGRAPHY IN NETWORK SECURITY
ELE548 Research Essays CRYPTOGRAPHY IN NETWORK SECURITY AUTHOR: SHENGLI LI INSTRUCTOR: DR. JIENCHUNG LO Date: March 5, 1999 Computer network brings lots of great benefits and convenience to us. We can
More informationElements of Applied Cryptography Public key encryption
Network Security Elements of Applied Cryptography Public key encryption Public key cryptosystem RSA and the factorization problem RSA in practice Other asymmetric ciphers Asymmetric Encryption Scheme Let
More informationData management using Virtualization in Cloud Computing
Data management using Virtualization in Cloud Computing A.S.R. Krishna Kanth M.Tech (CST), Department of Computer Science & Systems Engineering, Andhra University, India. M.Sitha Ram Research Scholar Department
More informationA novel deniable authentication protocol using generalized ElGamal signature scheme
Information Sciences 177 (2007) 1376 1381 www.elsevier.com/locate/ins A novel deniable authentication protocol using generalized ElGamal signature scheme WeiBin Lee a, ChiaChun Wu a, WoeiJiunn Tsaur
More informationAn Efficient Approach for Cost Optimization of the Movement of Big Data
2015 by the authors; licensee RonPub, Lübeck, Germany. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
More informationData LocalityAware Query Evaluation for Big Data Analytics in Distributed Clouds
Data LocalityAware Query Evaluation for Big Data Analytics in Distributed Clouds Qiufen Xia, Weifa Liang and Zichuan Xu Research School of Computer Science Australian National University, Canberra, ACT
More informationSCHEDULING IN CLOUD COMPUTING
SCHEDULING IN CLOUD COMPUTING Lipsa Tripathy, Rasmi Ranjan Patra CSA,CPGS,OUAT,Bhubaneswar,Odisha Abstract Cloud computing is an emerging technology. It process huge amount of data so scheduling mechanism
More informationSurvey on Efficient Information Retrieval for Ranked Query in CostEfficient Clouds
Survey on Efficient Information Retrieval for Ranked Query in CostEfficient Clouds Ms. Jyotsna T. Kumbhar 1 ME Student, Department of Computer Engineering, TSSM S, P.V.P.I.T., Bavdhan, Pune University,
More informationCryptography and Network Security Chapter 10
Cryptography and Network Security Chapter 10 Fifth Edition by William Stallings Lecture slides by Lawrie Brown (with edits by RHB) Chapter 10 Other Public Key Cryptosystems Amongst the tribes of Central
More informationA New Credit Card Payment Scheme Using Mobile Phones Based on Visual Cryptography
A New Credit Card Payment Scheme Using Mobile Phones Based on Visual Cryptography ChaoWen Chan and ChihHao Lin Graduate School of Computer Science and Information Technology, National Taichung Institute
More informationPrivacy Preservation and Secure Data Sharing in Cloud Storage
OPEN ACCESS Int. Res. J. of Science & Engineering, 2015; Vol. 3 (6): 231236 ISSN: 23220015 RESEARCH ARTICLE Privacy Preservation and Secure Data Sharing in Cloud Storage Chavhan Bhaurao* and Deshmukh
More informationStudy of algorithms for factoring integers and computing discrete logarithms
Study of algorithms for factoring integers and computing discrete logarithms First IndoFrench Workshop on Cryptography and Related Topics (IFW 2007) June 11 13, 2007 Paris, France Dr. Abhijit Das Department
More informationData Encryption A B C D E F G H I J K L M N O P Q R S T U V W X Y Z. we would encrypt the string IDESOFMARCH as follows:
Data Encryption Encryption refers to the coding of information in order to keep it secret. Encryption is accomplished by transforming the string of characters comprising the information to produce a new
More informationCryptography and Network Security Department of Computer Science and Engineering Indian Institute of Technology Kharagpur
Cryptography and Network Security Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Module No. # 01 Lecture No. # 05 Classic Cryptosystems (Refer Slide Time: 00:42)
More informationCOST MINIMIZATION OF RUNNING MAPREDUCE ACROSS GEOGRAPHICALLY DISTRIBUTED DATA CENTERS
COST MINIMIZATION OF RUNNING MAPREDUCE ACROSS GEOGRAPHICALLY DISTRIBUTED DATA CENTERS Ms. T. Cowsalya PG Scholar, SVS College of Engineering, Coimbatore, Tamilnadu, India Dr. S. Senthamarai Kannan Assistant
More informationImplementation and Comparison of Various Digital Signature Algorithms. Nazia Sarang Boise State University
Implementation and Comparison of Various Digital Signature Algorithms Nazia Sarang Boise State University What is a Digital Signature? A digital signature is used as a tool to authenticate the information
More informationEmbedding more security in digital signature system by using combination of public key cryptography and secret sharing scheme
International Journal of Computer Sciences and Engineering Open Access Research Paper Volume4, Issue3 EISSN: 23472693 Embedding more security in digital signature system by using combination of public
More information