The week's pick
Reseach Article
Privacy Preserving Dynamic Recommender System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 105 - Number 9 |
| Year of Publication: 2014 |
| Authors: Umakant L Tupe, R.b. Joshi |
10.5120/18409-9685
|
Umakant L Tupe, R.b. Joshi . Privacy Preserving Dynamic Recommender System. International Journal of Computer Applications. 105, 9 ( November 2014), 40-43. DOI=10.5120/18409-9685
Abstract
A Recommender System is now becoming main decision maker in today's word. It provides information for specific items such as books, news, cloths and many more. Personalization is now becoming common term for improving e-commerce services and attract more users. Todays recommender system provides suggestion for specific items but drawback that service provider can increases the ratings of specific product and unnecessarily popularity increases. This leads to misguiding the users while purchasing some products, so privacy is violated. Our main aim is to preserve privacy, so we have used homomorphic encryption scheme which uses no. of public private keys to preserve privacy. We have used PSP to remove active participation of user in encryption and decryption. In this paper we propose a cryptographic solution for preserving privacy of customers in recommender system. In short private information of customer is kept secret and service provider generates recommendation by processing encrypted data.
References
- "Generating Private recommendation using Homomorphic Encryption and Data Packing". IEEE TR ANS AC TIONS ON INF OR MAT ION F ORE NS IC S AND S EC UR ITY, VOL. 7, NO. 3 , J UNE 2 01 2 10 53
- G. Adomavicius and A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,"IEEE Trans. Knowl. Data Eng. , vol. 17, no. 6, pp. 734–749, Jun. 2005.
- N. Ramakrishna, B. J. Keller, B. J. Mirza, A. Y. Grama, and G. Karypis, "Privacy risks in recommender systems," IEEE Internet Comput. , vol. 5, no. 6, pp. 54–63, Nov 2001.
- R. Agrawal and R. Srikant, "Privacy-preserving data mining," in Proc. SIGMOD Rec. , May 2000, vol. 29
- Y. Lindell and B. Pinkas, "Privacy preserving data mining," J. Cryptol. , pp. 36–54, 2000, Springer-Verlag
- H. Polat and W. Du, "Privacy-preserving collaborative filtering using randomized perturbation techniques. ," in Proc. ICDM, 2003, pp. 625–628.
- H. Polat andW. Du, "SVD-based collaborative filtering with privacy," in Proc. 2005 ACM Symp. Applied Computing (SAC'05), New York, NY, 2005, pp. 791–795, ACM Press.
- S. Zhang, J. Ford, and F. Makedon, "Deriving private information from randomly perturbed ratings," in Proc. Sixth SIAM Int. Conf. Data
- R. Shokri, P. Pedarsani, G. Theodorakopoulos, and J. -P. Hubaux, "Preserving privacy in collaborative filtering through distributed aggregation of offline profiles," in Proc. Third ACM Conf. Recommender Systems (RecSys'09), New York, NY, 2009, pp. 157–164, ACM.
- F. Mc Sherry and I. Mironov, "Differentially private recommender systems: Building privacy into the net," in Proc. 15th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining , New York, NY, 2009, pp. 627–636, ACM.
- R. Cissée and S. Albayrak, "An agent-based approach for privacy preserving recommender systems," in Proc. 6th Int. Joint Conf. Autonomous Agents and Multiagent Systems (AAMAS'07), New York, NY, 2007, pp. 1–8, ACM.
- M. Atallah, M. Bykova, J. Li,K. Frikken, andM. Topkara, "Private collaborative forecasting and benchmarking," in Proc. 2004 ACM Workshop on Privacy in the Electronic Society (WPES'04), New York, NY, 2004, pp. 103–114, ACM.
- J. F. Canny, "Collaborative filtering with privacy. ," in IEEE Symp. Security and Privacy, 2002, pp. 45–57.
- J. F. Canny, "Collaborative filtering with privacy via factor analysis," in SIGIR. New York, NY: ACM Press, 2002,
- Z. Erkin, M. Beye, T. Veugen, andR. L. Lagendijk, "Privacy enhance recommender system," in Proc. Thirty-First Symp. Information Theory in the Benelux, Rotterdam, 2010,
- Z. Erkin, M. Beye, T. Veugen, and R. L. Lagendijk, "Efficiently computing private recommendations," in Proc. Int. Conf. Acoustic, Speech and Signal Processing (ICASSP), Prague, Czech Republic,May 2011,pp. 5864–5867, 2011.
- J. R. Troncoso-Pastoriza, S. Katzenbeisser, , "A secure multidimensional point inclusion protocol," in Proc. ACM Workshop on Multimedia and Security, 2007, pp. 109–120.
- T. Bianchi, A. Piva, and M. Barni, "Composite signal representatio for fast and storage-efficient processing of encrypted signals," IEEE Trans. Inf. Forensics Security, vol. no. 1, pp. 180–187, Mar. 2010.
Index Terms
Keywords