P. Jessup, Big data and targeted advertising, 2012.

J. Yap, User profiling fears real but paranoia unnecessary http://www.zdnet.com/user-profiling-fears-real-but-paranoia- unnecessary-2062302030, 2011.

A. Narayanan and V. Shmatikov, Robust De-anonymization of Large Sparse Datasets, 2008 IEEE Symposium on Security and Privacy (sp 2008), pp.111-125, 2008.
DOI : 10.1109/SP.2008.33

J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs et al., Big data: The next frontier for innovation, competition, and productivity The McKinsey Global Institute, 2011.

O. Hasan, L. Brunie, E. Bertino, and N. Shang, A Decentralized Privacy Preserving Reputation Protocol for the Malicious Adversarial Model, IEEE Transactions on Information Forensics and Security, vol.8, issue.6, pp.10-1109, 2013.
DOI : 10.1109/TIFS.2013.2258914
URL : https://hal.archives-ouvertes.fr/hal-01339185

S. T. Peddinti and N. Saxena, On the limitations of query obfuscation techniques for location privacy, Proceedings of the 13th international conference on Ubiquitous computing, UbiComp '11, p.187, 2011.
DOI : 10.1145/2030112.2030139

S. Brin and L. Page, The anatomy of a large-scale hypertextual Web search engine, Computer Networks and ISDN Systems, pp.107-117, 1998.
DOI : 10.1016/S0169-7552(98)00110-X

C. Dwork, Differential Privacy, pp.1-12, 2006.
DOI : 10.1007/11787006_1

A. Narayanan and V. Shmatikov, Robust De-anonymization of Large Sparse Datasets, 2008 IEEE Symposium on Security and Privacy (sp 2008), pp.111-125, 2008.
DOI : 10.1109/SP.2008.33

J. Canny, Collaborative filtering with privacy, Proceedings 2002 IEEE Symposium on Security and Privacy, p.45, 2002.
DOI : 10.1109/SECPRI.2002.1004361
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.16.3270

D. Li, Q. Lv, H. Xia, L. Shang, T. Lu et al., Pistis: A Privacy-Preserving Content Recommender System for Online Social Communities, 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, pp.79-86, 2011.
DOI : 10.1109/WI-IAT.2011.136

A. Boutet, D. Frey, A. Jegou, and A. Kermarrec, Privacy-Preserving Distributed Collaborative Filtering, INRIA, 2013.
DOI : 10.1007/978-3-319-09581-3_12
URL : https://hal.archives-ouvertes.fr/hal-01251314

A. Kobsa, LNCS 4321 -Privacy-Enhanced Web Personalization, pp.628-670, 2007.
DOI : 10.1007/978-3-540-72079-9_21
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.299.1633

R. Dingledine, N. Mathewson, and P. Syverson, Tor: the secondgeneration onion router, 2004.

F. Kerschbaum, A verifiable, centralized, coercion-free reputation system, Proceedings of the 8th ACM workshop on Privacy in the electronic society, WPES '09, pp.61-70, 2009.
DOI : 10.1145/1655188.1655197
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.612.327

J. Bethencourt, E. Shi, and D. Song, Signatures of Reputation, Proc. of the Intl. Conf. on Financial Cryptography (FC '10), pp.400-407, 2010.
DOI : 10.1007/978-3-642-14577-3_35

O. Hasan, L. Brunie, and E. Bertino, Preserving privacy of feedback providers in decentralized reputation systems, Computers & Security, vol.31, issue.7, pp.816-826, 2012.
DOI : 10.1016/j.cose.2011.12.003
URL : https://hal.archives-ouvertes.fr/hal-01352961

C. Dwork, Differential Privacy: A Survey of Results, pp.1-19, 2008.
DOI : 10.1007/978-3-540-79228-4_1

K. Nissim, Private Data Analysis via Output Perturbation, Privacy- Preserving Data Mining: Models and Algorithms, pp.383-405, 2008.
DOI : 10.1007/978-0-387-70992-5_16

H. Brenner and K. Nissim, Impossibility of Differentially Private Universally Optimal Mechanisms, 51th Annual IEEE Symposium on Foundations of Computer Science, pp.71-800256, 1008.
DOI : 10.1137/110846671
URL : http://arxiv.org/abs/1008.0256

R. Sarathy and K. Muralidhar, Evaluating Laplace Noise Addition to Satisfy Differential Privacy for Numeric Data, Transactions on Data Privacy, vol.4, issue.1, pp.1-17, 2011.
DOI : 10.1007/978-3-642-15838-4_19

L. Sankar, S. R. Rajagopalan, and H. V. Poor, Utility-Privacy Tradeoffs in Databases: An Information-Theoretic Approach, IEEE Transactions on Information Forensics and Security, vol.8, issue.6, pp.838-852, 2013.
DOI : 10.1109/TIFS.2013.2253320

C. Chang, B. Thompson, H. W. Wang, and D. Yao, Towards publishing recommendation data with predictive anonymization, Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security, ASIACCS '10, p.24, 2010.
DOI : 10.1145/1755688.1755693

C. A. Ardagna, G. Livraga, and P. Samarati, Protecting Privacy of User Information in Continuous Location-Based Services, 2012 IEEE 15th International Conference on Computational Science and Engineering, pp.162-169, 2012.
DOI : 10.1109/ICCSE.2012.31

Z. Luo, S. Chen, and Y. Li, A distributed anonymization scheme for privacy-preserving recommendation systems, 2013 IEEE 4th International Conference on Software Engineering and Service Science. IEEE, pp.491-494, 2013.

P. Samarati, Protecting respondents identities in microdata release, IEEE Transactions on Knowledge and Data Engineering, vol.13, issue.6, pp.1010-1027, 2001.
DOI : 10.1109/69.971193
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.226.7472

L. Sweeney, A. Model, . N. Protecting, W. Li, D. Qardaji et al., k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY, Proceedings of the 7th ACM Symposium on . . . , 2012. [Online]. Available, pp.557-570, 2002.
DOI : 10.1109/RISP.1993.287632

D. Chaum, Untraceable electronic mail, return addresses, and digital pseudonyms, Communications of the ACM, vol.24, issue.2, pp.84-90, 1981.
DOI : 10.1145/358549.358563
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.8210

C. W. Murugesan, /. Mummoorthy, and . Adviser-clifton, Privacy through deniable search, 2010.
DOI : 10.1137/1.9781611972795.66
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.4901

H. Pang, X. Ding, and X. Xiao, Embellishing text search queries to protect user privacy, Proceedings of the VLDB Endowment, pp.598-607, 2010.
DOI : 10.14778/1920841.1920918
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.174.5524

A. Nandi, A. Aghasaryan, M. Bouzid, S. Shang, Y. Hui et al., P3: A privacy preserving personalization middleware for recommendation-based services Hot Topics in Privacy Available: http://petsymposium.org Privacy Preserving Recommendation System Based on Groups, pp.1-12, 2011.