Weighted Graph Approach for Trust Reputation Management

Transcription

1 Weighted Graph Approach for Reputation Management K.Thiagarajan, A.Raghunathan, Ponnammal Natarajan, G.Poonkuzhali and Prashant Ranjan Abstract In this paper, a two way approach of developing trust between an agent and its client is followed where the client s priority is given more precedence than agent s priority. A detailed unique trust is calculated based on weighted signed graph for all possibilities of agent interacting its client and vice versa. Finally, the obtained result implies client produces best trust s than agents. This concept of trust applied for Spam filtering in services is discussed. Keywords Agent, Client, Reputation Management, Weighted Signed Graph, TLR ( Level Rating). I. INTRODUCTION EPUTATION management is the process of tracking an R entity's actions and other entities' opinions about those actions; reporting on those actions and opinions; and reacting to that report creating a feedback loop. Reputation management systems use various predefined criteria for processing complex data to report reputation. Nowadays, reputation management plays a vital role in all business activities. There are many techniques by which trust reputation management can be implemented. Although, the existing methodologies for trust reputation management provides better consistency than the conventional security approaches for the interaction among agents in a distributed networking environment, there are problems such as spurious, selfishness, autonomy, cooperation etc., are to be addressed yet. This paper discusses the trust reputation management through Weighted Signed Graph approach to K.Thiagarajan is the Rajalakshmi Engineering College in the Department of Mathematics, Affiliated to Anna University Chennai, TamilNadu, India, phone: vidhyamannan@yahoo.com A.Raghunathan is the Rajalakshmi Engineering College in the Department of Computer science and Engineering, Affiliated to Anna University Chennai, Tamil Nadu, India, raghucse@ymail.com Ponnammal Natarajan is the Rajalakshmi Engineering in the Department of Computer Science and Engineering, Affiliated to Anna University Chennai, Tamil Nadu, India, and pamman01@yahoo.com G.Poonkuzhali is the Rajalakshmi Engineering College in the Department of Computer Science and Engineering, Affiliated to Anna University Chennai, Tamil Nadu, India, phone: , Kuzhal_s@yahoo.co.in Prashant Ranjan is the Rajalakshmi Engineering College in the Department of Computer science and Engineering, Affiliated to Anna University Chennai, Tamil Nadu, India, prashanthranjan@gmail.com. improve the efficiency, security and cooperation among agents in a large network. Today in the world of Internet and other distributed networking systems, interaction independent third party agents are increased drastically due to many reasons such as ecommerce, credit card transactions, knowledge sharing etc. In a distributed networking environment each agent in the system can validate the other agent s reliability in two ways, either by hard security approaches such as password protection, digital signatures, keyword identification or by Soft security approach of developing trust and faith on the other agent. Generally the soft security approach is preferred, because hard security approaches does not provide optimal results in finding a right client or agent; however, the soft security approach of developing trust between an agent and a client also faces the following problems 1. Only the client s based on trust is taken into consideration. Principle agent's precedence to the clients is neglected [1]-[3]. 2. Spurious s generated by malicious clients are not taken into account. 3. Difference in assigning trust to an agent in small and big community network 4. Modeling a user by the agent is not dealt. 5. Selfishness, Autonomy and Cooperation [9]. This paper overcomes the above drawbacks of reputation management systems by weighted signed graph approach. The methodology of using signed graph for the betterment of the trust reputation management is discussed in the following sections. Finally, this approach of trust for filtering Spam in services is discussed. Outline of the Paper: Section 1 presents Introduction, Section 2 deals flow diagram of the proposed system, Section 3 presents Signed Graph method of trust, Section 4 presents Generalized Model for Estimating Level Rating in a Weighted Graph, and Section 5 presents Experimental results. Finally Section 6 presents conclusion and future enhancements. 830

2 II. FLOW DIAGRAM OF PROPOSED SYSTEM TABLE I SPECIFICATION OF CONSTRAINTS R 1 = (P 1 +P 2 +P 3 +P 4 )/4 R 2 = ( P 1 + P 2 + P 3 +P 5 )/4 R 3 = ( P 2 + P 3 +2 P 5 )/4 R 4 = ( P 3 +3 P 5 )/4 R 5 = 4 P 5 /4 = P 5 III.SIGNED GRAPH METHOD OF TRUST RATING In proposed system, distributed network is considered as a signed graph, where each node in the graph is considered as agents and each edge is assigned a weight called precedence of acceptance. Signed weight (++, +-, -+, --) is attached to each node of the graph based on the agent-client combination. The sign assigned to the agents tend to change according to the client which it interacts. Initially, all the agents are assigned ++ weight. Then the client are allowed to give their precedence of acceptance (++, +-,-+,--) over the specified agent. Depending upon the precedence given by clients, the trust of agents having positive trust positive attitude is calculated. Similarly agents having positive trust a negative attitude, negative trust positive attitude and negative trust a negative attitude is estimated. Similarly, all the clients are assigned ++ weight. Then the agent are allowed to give their precedence of acceptance (++, +-,-+,--) over the specified client. Depending upon the precedence given by agents, the trust of clients having positive trust positive attitude is calculated. Similarly clients having positive trust a negative attitude, negative trust positive attitude and negative trust a negative attitude is estimated. Since the precedence of acceptance from both agent and client are considered, the treat of spurious by a malicious agent is reduced. A malicious agent can be easily traced since it will have the worst from all its clients. In this approach, the client is emphasized superior over the agent and hence client's has given a better preference than the agent's. When a client does not completely trust an agent then the trust will be 0, even though the agent is having complete trust over the client. The various combinations of precedence of acceptance between an agent and a client are explained in the next section. IV. MATHEMATICAL FORMULATION Fig. 1 Flow Diagram of the Proposed System Let us consider there are 'n' number of agents in a network. Each agent want to interact 'i' number of clients in the same network. A >A 1, A 2, A 3... A n C >C n1, C n2, C n3...c ni Where, n and ni are positive integers. 831

3 TABLE II CONSTRAINT FIXING AND FLOW FIXING Precedence of acceptance Description Positive trust Positive attitude. Positive trust Negative attitude. Negative trust Positive attitude. Negative trust Negative attitude. Fig. 2 Interaction of agent A 1 clients C 1, C 2, C 3 the number of agents and corresponding clients connected to it. V. GENERALIZED MODEL FOR ESTIMATING RATING IN A WEIGHTED GRAPH In a weighted graph, P 1, P 2, P 3, P 4 and P 5 are considered as the trust s, where P 1 >P 2 > P 3 > P 4 > P 5. (i.e., P 1 is the highest trust and P 5 is the least trust ). If the values for trust is taken as P 1 =100, P 2 =75,P 3 =50, P 4 =25, P 5 =0, then the final decision will be based on the addition of these values divided by the number of agent-client or client-agent combinations. In such a case the complete trust will have a of 250/4, better trust will have a of 225/4, good trust will have a of 125/4, bad trust will have a of 50/4 and distrust will have a of 0. Similarly, if the values for P 1 =4, P 2 =3, P 3 =2, P 4 =1, P 5 =0 are assigned, then the complete trust will have a of 10/4, better trust will have a of 9/4, good trust will have a of 5/4, bad trust will have a of 2/4 and distrust will have a of 0. The ratio among the different trust decisions lies between 1 and 0 in decreasing order. The above assigned values for P i s have the trust ratio as 1: 0.9: 0.5: 0.2: 0. TABLE III RATING OF AGENT TO CLIENT Client Fig. 3 Interaction of agent A 2 clients C 1, C 2, C 3 Assume an agent A 1 wants to interact C 1, C 2 and C 3. Since the clients have different trust s according to the agent which it is interacting, by the expression (2), instance of clients are created i.e., C 11, C 12, C 13 for agent A 1 and C 21, C 22, C 23 for agent A 2. For example, Interaction between A 1 and C 1 may result in a best trust whereas interaction between A 2 and C 1 may give a worst trust. Hence the client s response to an agent is based on its precedence of acceptance. Here certain test cases are taken which the precedence of acceptance is assigned to the agents and the of trust is calculated. The first 16 figures from Fig 4 to Fig 7 represent the development of trust from the agent on the client and the next 16 figures from Fig 8 to Fig 11 represent the development of trust from the client on the agent. When an agent has complete trust on a client, and if the client also has complete trust on the agent then there will be a strong of trust between them, producing 100% trust. When an agent has ++ acceptance and client has + - of acceptance, then it means agent is willing to trust the client but the client is having a positive trust negative attitude on the agent, which results in a 75% trust. The detailed chart of trust scheme is given below in the tables 1 and 2. The final will be calculated according to Agent + + P 1 P 2 P 3 P P 2 P 3 P 3 P P 2 P 3 P 5 P P 3 P 5 P 5 P 5 TABLE IV RATING OF CLIENT TO AGENT Agent Client + + P 1 P 2 P 3 P P 2 P 3 P 3 P P 3 P 5 P 5 P P 5 P 5 P 5 P 5 832

4 TABLE V TRUST AGGREGATION: decision made ( P 1 + P 2 + P 3 + P 4 )/4 Complete ( P 1+ P 2+ P 3+ P 5)/4 Better ( P 2 + P 3 +2 P 5 )/4 Good ( P 3 +3 P 5 )/4 Bad 4 P 5/4 = P 5 Distrust AGENT VS CLIENT COMBINATIONS Fig. 5 Agent having trust over the client a negative attitude and client s trust changes TABLE VII RESULT OF RATING OF FIG P P 3 GOOD P P 5 ( P 2 + P 3 +2 P 5 )/4 Fig. 4 Agent having complete trust over the client and client s trust changes TABLE VI RESULT OF RATING OF FIG P P P P 5 BETTER (P 1+ P 2+ P 3+ P 5)/4 Fig. 6 Agent having negative trust over the client positive attitude and client s trust changes 833

5 TABLE VIII RESULT OF RATING OF FIG 6 Level P P P P 5 GOOD (P 2+ P 3+2P 5)/4 Fig. 8 Client having complete trust over the agent and agent s trust changes TABLE X RESULT OF RATING OF FIG 8 Level P P P 3 COMPLETE (P 1+ P 2+ P 3+ P 4)/ P 4 Fig. 7 Agent having distrust over the client. TABLE IX RESULT OF RATING OF FIG 7 Level P P 5 BAD P P 5 (P 3 +3 P 5 )/4 CLIENT VS AGENT COMBINATIONS: Fig. 9 Client having trust over the agent negative attitude and agent s trust changes 834

6 TABLE X1 RESULT OF RATING OF FIG P P P P 5 based on trust GOOD (P 2 + P 3 +2P 5 )/4 Fig. 11 Client having distrust over the agent TABLE XIII RESULT OF RATING OF FIG P P P 5 based on trust DIS 4 P 5 / P 5 V. EXPERIMENTAL RESULTS Here we assumed P 1 =30, P 2 =20, P 3 =15, P 4 =10, P 5 =5. Fig. 10 Client having negative trust over the agent positive attitude and agent s trust changes TABLE XII RESULT OF RATING OF FIG P P P P 5 based on trust BAD (P 3 +3P 5 )/4 Clien t TABLE XIV RATING BETWEEN AGENT VS CLIENT Agent Final Rating Calculated Client (P 1 +P 2 +P 3 +P 4 )/4 =75/ ( P 2 + 2P 3 +P 5 )/4 = 55/ ( P 2+ P 3+2 P 5)/4 =30/ (4 P 5 )/4 = 200/4 TABLE XV RATING BETWEEN CLIENT VS AGENT Final Rating Calculated Agen t (P 1+P 2+P 3+P 5)/4 = 70/ ( P 2+ P 3+2 P 5)/4 =45/ ( P 2 + P 3 +2 P 5 )/4 =45/ ( P 3+3 P 5)/4 = 30/4 Ratio obtained Decision Made 1.0 Complete 0.7 Good 0.4 Bad Distrust Ratio obtained Decision Made Better 0.6 Good 0.6 Good 0.4 Bad VI. REAL TIME APPLICATIONS This type of signed graph approach for reputation management can be used in the spam guards. Here the sender of a mail will be considered as an agent and the receiver will be considered as the client. The precedence of acceptance of the client will be assigned based on the content of the mail send by the sender. Similarly based on the agent s response 835

7 from the client, the precedence of acceptance of the agent will be assigned initially. Whenever the client receives a spam, the above approach can be applied to filter spam in a distributed network. VII. CONCLUSION AND FUTURE WORK In this study, trust reputation management through weighted signed graph is reemphasized the age old golden rule: The Client is supreme over the Agent. This method can be applied for finding redundancy in web pages, for removing spurious ranking of products in retail industry and in avoiding unwanted short messages in the mobile phones from the mobile networks. REFERENCES [1] Bin Yu, Munindar P.Singh, An Evidential model for distributed reputation Management, AAMAS 02, July 15-19,2002, Bologna, Italy. [2] B. Yu and M. P. Singh. and reputation management in a smallworld network. In Proceedings of the 4th International Conference on MultiAgent Systems, pages , [3] B.Yu and M.P.Singh. A social Mechanism of reputation management in electronic communities. In proceedings of the 4th International Workshop on Cooperative Information Agents, [4] E.Sampathkumar, (Eds: M.M Shikare and B.N.Waphare) Semigraphs Narosa Publishing house, New Delhi, (2004), [5] F.Harary Addision-Wesley, Reading Graph Theory, Mass, (1969). [6] G. Zacharia and P. Maes. management through reputation mechanisms. Applied Artificial Intelligence, 14: , [7] K. Aberer and Z. Despotovic. Managing trust in a peer-2-peer information system. In Proceedings of the 10th International Conference on Information and Knowledge Management (CIKM), pages , [8] L.Xiong and L.Liu. Vulnerabilities and security issues in structured overlay networks: A quantitative analysis. In proceedings of the Annual Computer Security Applications Conference (ACSAC), [9] Rajesh Turlapati; Huhns, M.N., Multiagent reputation management to achieve robust software using redundancy,intelligent Agent Technology, IEEE/WIC/ACM International Conference on Volume, Issue, Sept Page(s): [10] S.Kamvar, M.Schlosser, H. Garcia-Molina, Eigen : Reputation management in p2p networks. In proceedings of the 12th WWW [11] conference, [12] S.P.Srinivasan, P.Malliga, K.Thiagarajan, An Algorithmic Graphical approach for Ground logistics management of Jatropha seed distribution.,ultra Scientist of Physical Sciences, Vol. 20, No:3,(M)(2008), Page(s): [13] Zhengqiang Liang and Weisong Shi, Enforcing Cooperative Resource Sharing in Untrusted P2P Computing Environments Available: K.Thiagarajan working as Senior Lecturer in the Department of Mathematics in Rajalakshmi Engineering College- Chennai-India. He has totally 14 years of experience in teaching. He has attended and presented research articles in 33 National and International Conferences and published one national journal and 29 international journals. Currently he is working on web mining through automata and set theory. His area of specialization is coloring of graphs and DNA Computing. A.Raghunathan is a final year B.E student of Rajalakshmi Engineering College- Chennai, India. He has presented 3 Papers in National symposiums and a paper in an international Conference. He is a student member of Computer Society of India (CSI). Ponnammal Natarajan finished her Ph.D from Texas A&M University in the year She has more than 35 years of experience in teaching and research guidance. She is former Director of research, Anna University Chennai-India. Currently Advisor, (Research and Development), Rajalakshmi Engineering College Chennai- India. She has conducted many national and International workshops, Seminars, Symposiums and Conferences. G.Poonkuzhali received B.E degree in Computer Science and Engineering from University of Madras, Chennai, India, in 1998, and the M.E degree in Computer Science and Engineering from Sathyabama University, Chennai, India, in Currently she is pursuing Ph.D programme in the Department of Information and Communication Engineering at Anna University Chennai, India. She has presented and published 3 international journals and authored 5 books. She is a life member of ISTE (Indian Society for Technical Education) and CSI (Computer Society of India). Prashant Ranjan is a final year B.E student of Rajalakshmi Engineering College- Chennai, India. He has presented 4 Papers in National symposiums and a paper in an international Conference. He is a student member of Computer Society of India (CSI) 836