# Seclusion Search over Encrypted Data in Cloud Storage Services

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5 Symmetric Searchable Encryption Curtmolaet al. proposed SSE, which is a deterministic symmetric key encryption scheme with security guarantees under rigorous definitions [6]. The SSE scheme is based on the inverted index and uses pseudorandom functions/per Top-down methodology for designing privacy-assured search schemes. Methodology we describe a top down approach in Fig. 2. Given a search functionality in the plaintext domain, one can decompose it into a certain data index structure and primitive data operations using relevant information retrieval (IR) principles. Then we can try to find a proper encryption scheme to encrypt the data while simultaneously allowing data operations. The second step is nontrivial. Although efficiency could be improved due to the adopted index structure, care needs to be taken to prevent leakage of private information to the cloud server, especially when the server possesses background information on the query statistics. As a result, sometimes the encryption primitive itself may need to be adapted to meet mutations, which makes the search quite efficient. Roughly speaking, the index consists of blinded keywords f k (w i ) and lists of FIDs containing w i, where f() is a pseudorandom function and k is the secret key. The search trapdoor is also in the same form so that the server can perform matching. However, it only supports single keyword exact query. Order-Preserving Symmetric Encryption In OPSE [2], the numerical ordering of plaintext is preserved after encryption. Boldyrevaet al. [2] provide the first cryptographic construction of OPSE that is provably secure under the security framework of pseudorandom function or pseudorandom permutation. It can be regarded as a function g( ) from a domain D = {1 M} to a range R = {1 N}. 1. One To ManyOrder PreservingMapping OPM Algorithm 1: procedure OPMK(wi)(Sij(D,R,m, id (F)) 2: while D! = 1 do 3: {D,R} <--BinarySearch (K,D,R,m); 4: end while 5: coin < R --TapeGen(K, (D,R, 1 m, id(f))); 6: c < coin R; 7: return c; 8: end procedure 9: procedure BinarySearch(K,D,R,m); 10: M <-- D, N<-- R ; 11: d min(d) 1, r< min(r) 1; 12: y r + [N/2]; 13: coin< R TapeGen (K, (D,R, 0 y)); 14: x< R -- d + HYGEINV(coin,M,N, y - r); 15: if m x then 16: D {d x}; 17: R {r + 1,..., y}; 18: else 19: D {x +1...d+M} ; 20: R {y r + N}; 21: end if 22: return {D, R}, 23: end procedure Achieving Secure Ranked Search over Encrypted Data an especially important functionality in plaintext IR is to support ranking mechanisms over search results according to user specified relevance criteria. Usually, this is achieved by building an inverted index (index structure) and adopting a ranking function to compute the rank of each file relevant to a given search request (primitive data operations are keyword matching and sorting). For example, the following function can be used: 2015, IJCSMC All Rights Reserved 31

6 Figure 3. Top-down methodology for designing privacy-assured search schemes. as statistical measurement for evaluating relevance score in the information retrieval community uses the TF IDF rule, where term frequency (TF) is simply the number of times a given term or keyword (we will use them interchangeably hereafter) appears within a file (to measure the importance of the term within the particular file), and inverse document frequency(idf) is obtained by dividing the number of files in the whole collection by the number of files containing the term (to measure the overall importance of the term within the whole collection). Here, Q denotes the searched keywords; fd;t denotes the TF of term t in file Fd ; ft denotes the number of files that contain term t; N denotes the total number of files in the collection; and F d is the length of file F d, obtained by counting the number of indexed terms, functioning as the normalistion factor. Toward achieving secure ranked search in the encrypted domain, Zerret al. [16] observed that although the posting list elements (document IDs that contain each keyword in an inverted index) are encrypted, the term frequency distribution for each posting list can still help adversaries re identify the keyword. Thus, they proposed to transform the relevance scores such that their distribution is uniform for each keyword. They show that this scheme satisfies a definition called r confidentiality in a statistical sense. However, it requires much pre-processing and does not easily handle score dynamics, while the security level is weak. To improve both the efficiency and privacy, Wang et al. proposed a ranked symmetric searchable encryption (RSSE) scheme [12] that enables result ranking for single keyword query. To ensure privacy, a straightforward yet ideally secure RSSE scheme can be derived based on the existing SSE solution in [6], but requires two rounds of interactions between the user and the cloud server, which incurs high communication overhead. Thus, they adopt OPSE [2] to obtain practical performance, where the numerical ordering of the plaintexts is preserved after encryption. Specifically, during the search operation the relevance order (OPSE encrypted relevance scores) of each document is revealed to the server. In this way, efficient relevance score ranking can be done as in the plaintext domain. However, because the original OPSE is a deterministic encryption scheme, this still leaks much information. If the server has some background information on the dataset, such as the distribution of relevance scores for each plaintext keyword, it could reverse engineer the keyword. To break this determinacy, the authors propose one tomanyorder preserving mapping (OPM), which maps the same relevance score to different encrypted values. They incorporate the unique file IDs together with the plaintext as the random seed in the final ciphertext chosen process in OPSE. Thus, the same plaintext will no longer be deterministically assigned to the same ciphertext, but instead to a random value within the randomly assigned bucket in a range R. Furthermore, they use different keys to encrypt the relevance score for different posting lists (documents containing each keyword) to make the OPM more indistinguishable. Therefore, we have to modify the OPSE to suit our purpose. In order to reduce the amount of information leakage from the deterministic property, an one to many OPSE scheme is thus desired, which can flatten or obfuscate the original relevance score distribution, increase its randomness, and still preserve the plaintext order. To do so, we first briefly review the encryption process of original deterministic OPSE, where a plaintext m in domain D is always mapped to the same random sizednon overlapping interval bucket in range R, determined by a keyed binary search over the range R and the result of a random HGD sampling function. A ciphertext c is then chosen within the bucket by using m as the seed for some random selection function. Our one to manyorder preserving mapping employs the random plaintext to bucket mapping of OPSE, but incorporates the unique file IDs together with the plaintext m as the random seed in the final ciphertext chosen process. Due to the use of unique file ID as part of random selection seed, the same plaintext m will no longer be deterministically assigned to the same ciphertext c, but instead a random value within the randomly assigned bucket in range R. The whole process is shown 2015, IJCSMC All Rights Reserved 32

8 well behind symmetric key based solutions. Finally, it is also interesting to ask if more complex data utilization functions can be efficiently evaluated on encrypted data; For example, running database join/merge queries, or graph algorithms on structured data. VII. CONCLUDING REMARKS In this article, we identify the problem and challenges of enabling privacy assured searchable cloud data storage services. Recent research advances in this field are surveyed, which suggest that achieving functionally rich, usable, and efficient search on encrypted data is possible without sacrificing privacy guarantee too much. The steady evolution of this field will need to bring expertise from the cryptography, database, and information retrieval communities. REFERENCES [1] S. Zerr et al., Zerber+r: Top-k Retrieval from a Confidential Index, Proc. EDBT 09, [2] A. Boldyreva et al., Order-Preserving Symmetric Encryption, Proc. Eurocrypt 09, LNCS, vol. 5479, Springer, [3] N. Cao et al., Privacy-PreservingMulti-Keyword Ranked Search Over Encrypted Cloud Data, IEEE INFOCOM, 2011, pp [4] N. Cao et al., Privacy-Preserving Query over Encrypted Graph Structured Data in Cloud Computing, 31st Int l. Conf. Distributed Computing Systems, 2011, pp [5] M. Chase and S. Kamara, Structured Encryption and Controlled Disclosure, Advances in Cryptology-ASIACRYPT 2010, 2010, pp [6] R. Curtmola et al., Searchable Symmetric Encryption: Improved Definitions and Efficient Constructions, Proc. ACM CCS 06, [7] C. Dwork, Differential Privacy, Automata, Languages and Programming, 2006, pp [8] S. Kamara and K. Lauter, Cryptographic Cloud Storage, Proc. 14th Int l. Conf. Financial Cryptography and Data Security, Berlin, Heidelberg, 2010, pp [9] J. Li et al., Fuzzy Keyword Search over Encrypted Data in Cloud Computing, Proc. IEEE INFOCOM 10 MiniConf. San Diego, CA, Mar [10] M. Li et al., Authorized Private Keyword Search over Encrypted Data in Cloud Computing, 31st Int l. Conf. Distributed Computing Systems, 2011, pp [11] D. Song, D. Wagner, and A. Perrig, Practical Techniques for Searches on Encrypted Data, Proc. IEEE S & P 2000, [12] C. Wang et al., Secure Ranked Keyword Search Over Encrypted Cloud Data, Proc. ICDCS 10, [13] C. Wang et al., Toward Publicly Auditable Secure Cloud Data Storage Services, IEEE Network, vol. 24, no. 4, July Aug. 2010, pp [14] C. Wang et al., Achieving Usable and Privacy-Assured Similarity Search over Outsourced Cloud Data, Proc. IEEE INFOCOM 12, Orlando, FL, Mar [15] W. K. Wong et al., Secure KNN Computation on Encrypted Databases, Proc. SIGMOD, [16] M. Armbrust et al., Above the Clouds: A Berkeley View of Cloud Computing, Feb [17] Challenges toward Achieving Privacy and Secure Searchable Outsource cloud data Storage Services, IJARCSSE 2015, IJCSMC All Rights Reserved 34

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