D. E. Bakken, R. Rarameswaran, D. M. Blough, A. A. Franz, and T. J. Palmer, Data obfuscation: anonymity and desensitization of usable data sets, IEEE Security and Privacy Magazine, vol.2, issue.6, pp.34-41, 2004.
DOI : 10.1109/MSP.2004.97

A. Bakker, E. Ogston, and M. Van-steen, Collaborative filtering using random neighbours in peer-to-peer networks, Proceeding of the 1st ACM international workshop on Complex networks meet information & knowledge management, CNIKM '09, pp.67-75, 2009.
DOI : 10.1145/1651274.1651288

M. Robert, Y. Bell, and . Koren, Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights, Proceedings of the 2007 Seventh IEEE International Conference on Data Mining, ICDM '07, pp.43-52, 2007.

A. Bellogín and K. , Recommender System Performance Evaluation and Prediction: An Information Retrieval Perspective, 2012.

A. Bellogin, P. Castells, and I. Cantador, Precisionoriented evaluation of recommender systems: an algorithmic comparison, Proceedings of the fifth ACM conference on Recommender systems, pp.333-336, 2011.

]. Berkovsky, Y. Eytani, T. Kuflik, and F. Ricci, Enhancing privacy and preserving accuracy of a distributed collaborative filtering, Proceedings of the 2007 ACM conference on Recommender systems , RecSys '07, pp.9-16, 2007.
DOI : 10.1145/1297231.1297234

C. Nicolo, G. Bianchi, and . Lugosi, Prediction, learning, and games, 2006.

M. Bilenko and M. Richardson, Predictive client-side profiles for personalized advertising, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pp.413-421, 2011.
DOI : 10.1145/2020408.2020475

Y. Blanco-fernández, J. J. Pazos-arias, A. Gil-solla, M. Ramos-cabrer, M. López-nores et al., Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study, Journal of Systems and Software, vol.81, issue.12, pp.2371-2385, 2008.
DOI : 10.1016/j.jss.2008.05.009

P. Borlund, The concept of relevance in IR, Journal of the American Society for Information Science and Technology, vol.9, issue.10, pp.913-925, 2003.
DOI : 10.1002/asi.10286

L. Bottou and O. Bousquet, The Tradeoffs of Large Scale Learning, Advances in Neural Information Processing Systems, pp.161-168, 2008.

C. Buckley and G. Salton, Optimization of relevance feedback weights, Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '95, pp.351-357, 1995.
DOI : 10.1145/215206.215383

]. J. Calandrino, A. Kilzer, A. Narayanan, E. W. Felten, and V. Shmatikov, "You Might Also Like:" Privacy Risks of Collaborative Filtering, 2011 IEEE Symposium on Security and Privacy, pp.231-246, 2011.
DOI : 10.1109/SP.2011.40

S. Castagnos and A. Boyer, Modeling Preferences in a Distributed Recommender System, UM '07: Proceedings of the 11th international conference on User Modeling, pp.400-404, 2007.
DOI : 10.1007/978-3-540-73078-1_53
URL : https://hal.archives-ouvertes.fr/inria-00171802

C. Xiaoqiang and V. Huang, Privacy Preserving Data Publishing for Recommender System, Computer Software and Applications Conference Workshops (COMPSACW), 2012 IEEE 36th Annual, pp.128-133, 2012.

T. Robert, R. L. Clemen, and . Winkler, Combining Probability Distributions From Experts in Risk Analysis, Risk Analysis, vol.19, issue.2, pp.187-203, 1999.

S. Graham-cormode and . Muthukrishnan, An improved data stream summary: the count-min sketch and its applications, Journal of Algorithms, vol.55, issue.1, pp.58-75, 2005.
DOI : 10.1016/j.jalgor.2003.12.001

L. Cranor, M. Langheinrich, M. Marchiori, M. Presler-marshall, and J. Reagle, The Platform for Privacy Preferences 1.0 (P3P1.0) Specification, 2002.

]. F. Crestani, Application of Spreading Activation Techniques in Information Retrieval, Artificial Intelligence Review, vol.11, issue.6, pp.453-482, 1997.
DOI : 10.1023/A:1006569829653

I. Damgård and M. Jurik, A Generalisation, a Simplification and some Applications of Paillier???s Probabilistic Public-Key System, BRICS Report Series, vol.7, issue.45, pp.119-136, 1992.
DOI : 10.7146/brics.v7i45.20212

S. Abhinandan, M. Das, A. Datar, S. Garg, and . Rajaram, Google news personalization: scalable online collaborative filtering, Proceedings of the 16th international conference on World Wide Web, WWW '07, pp.271-280, 2007.

S. Dasiopoulou, E. Giannakidou, G. Litos, P. Malasioti, and Y. Kompatsiaris, A Survey of Semantic Image and Video Annotation Tools, Lecture Notes in Computer Science, vol.5, issue.8, pp.196-239, 2011.
DOI : 10.1007/11930334_4

J. Davidson, B. Liebald, J. Liu, P. Nandy, T. Van-vleet et al., The YouTube video recommendation system, Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pp.293-296, 2010.
DOI : 10.1145/1864708.1864770

D. Prete-2010-]-l, L. Del-prete, and . Capra, diffeRS: A Mobile Recommender Service, Mobile Data Management (MDM) Eleventh International Conference on, pp.21-26, 2010.

D. Noia-2014-]-tommaso-di-noia, I. Cantador, and V. C. Ostuni, Linked Open Data-enabled Recommender Systems: ESWC 2014 Challenge on Book Recommendation

N. Dokoohaki and C. Kaleli, Achieving Optimal Privacy in Trust-Aware Social Recommender Systems, Lecture Notes in Computer Science, vol.6430, issue.54, pp.62-79, 2010.
DOI : 10.1007/978-3-642-16567-2_5

F. Draidi, E. Pacitti, and B. Kemme, P2Prec, Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM '11, pp.87-116, 2011.
DOI : 10.1145/2063576.2064028
URL : https://hal.archives-ouvertes.fr/inria-00543298

L. Duboc, D. Rosenblum, and T. Wicks, A framework for characterization and analysis of software system scalability, Proceedings of the the 6th joint meeting of the European software engineering conference and the ACM SIGSOFT symposium on The foundations of software engineering , ESEC-FSE '07, pp.375-384, 2007.
DOI : 10.1145/1287624.1287679

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

C. Dwork, M. Naor, T. Pitassi, and G. N. Rothblum, Differential privacy under continual observation, Proceedings of the 42nd ACM symposium on Theory of computing, STOC '10, pp.715-724, 2010.
DOI : 10.1145/1806689.1806787
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.5314

M. Ehrig, P. Haase, M. Hefke, and N. Stojanovic, Similarity for Ontologies -a Comprehensive Framework In In Workshop Enterprise Modelling and Ontology: Ingredients for Interoperability, at PAKM, 2004.

M. Ahmed, D. Elmisery, and . Botvich, Privacy Aware Recommender Service for IPTV Networks, Proceedings of the 2011 Fifth FTRA International Conference on Multimedia and Ubiquitous Engineering, pp.160-166, 2011.

Z. Erkin, T. Veugen, T. Toft, and R. L. Lagendijk, Generating Private Recommendations Efficiently Using Homomorphic Encryption and Data Packing. Information Forensics and Security, IEEE Transactions on, vol.7, issue.56, pp.1053-1066, 2012.
DOI : 10.1109/tifs.2012.2190726
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.398.4503

E. Commission, . Communication, . The, . To, . European et al., Safeguarding Privacy in a Connected World A European Data Protection Framework for the 21st Century, 2012.

N. Leonard and . Foner, Political Artifacts and Personal Privacy: The Yenta Multi-Agent Distributed Matchmaking System, Program in Media Arts and Sciences, School of Architecture and Planning, Massachusetts Institute of Technology, 1999.

Z. Gantner, L. Drumond, C. Freudenthaler, S. Rendle, and L. Schmidt-thieme, Learning Attribute-to-Feature Mappings for Cold-Start Recommendations, 2010 IEEE International Conference on Data Mining, pp.176-185, 2010.
DOI : 10.1109/ICDM.2010.129

A. Gunawardana and C. Meek, A unified approach to building hybrid recommender systems, Proceedings of the third ACM conference on Recommender systems, RecSys '09, pp.117-124, 2009.
DOI : 10.1145/1639714.1639735

M. Halkidi and I. Koutsopoulos, A Game Theoretic Framework for Data Privacy Preservation in Recommender Systems, Dimitrios Gunopulos, pp.629-644, 2011.
DOI : 10.1007/978-3-642-23780-5_50

]. P. Han, A scalable P2P recommender system based on distributed collaborative filtering, Expert Systems with Applications, vol.27, issue.2, pp.203-210, 2004.
DOI : 10.1016/j.eswa.2004.01.003

]. Hanani, B. Shapira, and P. Shoval, Information Filtering: Overview of Issues, Research and Systems, User Modeling and User-Adapted Interaction, vol.11, issue.3, pp.203-259, 2001.
DOI : 10.1023/A:1011196000674

]. T. Hoens, M. Blanton, and N. V. Chawla, A Private and Reliable Recommendation System for Social Networks, 2010 IEEE Second International Conference on Social Computing, pp.816-825, 2010.
DOI : 10.1109/SocialCom.2010.124

]. Hsu, M. Wen, H. Lin, C. Lee, and C. Lee, AIMED- A Personalized TV Recommendation System, Interactive TV: a Shared Experience, pp.166-174, 2007.
DOI : 10.1007/978-3-540-72559-6_18

A. Jeckmans, M. Beye, Z. Erkin, P. Hartel, R. Lagendijk et al., Privacy in Recommender Systems, Social Media Retrieval, Computer Communications and Networks, pp.263-281, 2013.
DOI : 10.1007/978-1-4471-4555-4_12

]. Jelasity, A. Montresor, O. Babaoglu, and . T-man, T-Man: Gossip-based fast overlay topology construction, Computer Networks, vol.53, issue.13, pp.2321-2339, 2009.
DOI : 10.1016/j.comnet.2009.03.013

]. Jiang and A. Tan, Learning and inferencing in user ontology for personalized Semantic Web search, Information Sciences, vol.179, issue.16, pp.2794-2808, 2009.
DOI : 10.1016/j.ins.2009.04.005

A. Kermarrec and V. Leroy, Application of Random Walks to Decentralized Recommender Systems, Principles of Distributed Systems, pp.48-63, 2010.
DOI : 10.1007/11549468_125
URL : https://hal.archives-ouvertes.fr/inria-00520214

J. K. Kim, K. Hyea, Y. H. Kim, and . Cho, A user-oriented contents recommendation system in peer-to-peer architecture, Expert Systems with Applications, vol.34, issue.1, pp.300-312, 2008.
DOI : 10.1016/j.eswa.2006.09.034

J. Nancy, P. W. King, and . Jessen, Profiling the mobile customer - Privacy concerns when behavioural advertisers target mobile phones -Part I, 2010.

R. Kohavi, R. Longbotham, D. Sommerfield, and R. Henne, Controlled experiments on the web: survey and practical guide, Data Mining and Knowledge Discovery, vol.33, issue.6, pp.140-181, 2009.
DOI : 10.1007/s10618-008-0114-1

Y. Koren and J. Sill, OrdRec, Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pp.117-124, 2011.
DOI : 10.1145/2043932.2043956

]. Lam, D. Frankowski, and J. Riedl, Do You Trust Your Recommendations? An Exploration of Security and Privacy Issues in Recommender Systems, Günter Müller, editeur, Emerging Trends in Information and Communication Security, pp.14-29, 2006.
DOI : 10.1007/11766155_2

]. Lang, NewsWeeder: Learning to Filter Netnews, Proceedings of the 12th International Conference on Machine Learning, pp.331-339, 1995.
DOI : 10.1016/B978-1-55860-377-6.50048-7
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.22.6286

N. Lathia, S. Hailes, and L. Capra, Private distributed collaborative filtering using estimated concordance measures, Proceedings of the 2007 ACM conference on Recommender systems , RecSys '07, pp.1-8, 2007.
DOI : 10.1145/1297231.1297233
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.103.4448

L. Pierre and . Ecuyer, Random Number Generation Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, pp.93-137, 2007.

]. Lieberman, Letizia: an agent that assists web browsing, Proceedings of the 14th international joint conference on Artificial intelligence 114, 1995.

]. G. Linden, B. Smith, and J. York, Amazon.com recommendations: item-to-item collaborative filtering, IEEE Internet Computing, vol.7, issue.1, pp.76-80, 2003.
DOI : 10.1109/MIC.2003.1167344

S. Magureanu, N. Dokoohaki, S. Mokarizadeh, and M. Matskin, Epidemic Trust-Based Recommender Systems, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing, pp.461-470, 2012.
DOI : 10.1109/SocialCom-PASSAT.2012.94

L. B. Marinho, A. Nanopoulos, L. Schmidt-thieme, and R. Jäschke, Andreas Hotho, Gerd Stumme and Panagiotis Symeonidis. Social Tagging Recommender Systems, pp.615-644, 2011.

P. Massa and P. Avesani, Trust Metrics in Recommender Systems Computing with Social Trust, Human- Computer Interaction Series, pp.259-285, 2009.

T. Matsumoto and . Nishimura, Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator, ACM Transactions on Modeling and Computer Simulation, vol.8, issue.1, pp.3-30, 1998.
DOI : 10.1145/272991.272995

]. Mcmahan, G. Holt, D. Sculley, M. Young, D. Ebner et al., Ad click prediction, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '13, pp.1222-1230, 2013.
DOI : 10.1145/2487575.2488200

E. Stuart, N. R. Middleton, D. C. Shadbolt, and . De-roure, Ontological user profiling in recommender systems, ACM Trans. Inf. Syst, vol.22, issue.13, pp.54-88, 2004.

N. Bradley, J. A. Miller, J. Konstan, and . Riedl, PocketLens: Toward a personal recommender system, ACM Transactions on Information Systems, vol.22, issue.3, pp.437-476, 2004.

A. Moreno, C. Ariza-porras, P. Lago, C. Jiménez-guar?n, H. Castro et al., Hybrid Model Rating Prediction with Linked Open Data for Recommender Systems, 2014.
DOI : 10.1007/978-3-319-12024-9_26
URL : https://hal.archives-ouvertes.fr/hal-01342085

]. 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

]. K. Oku, S. Nakajima, J. Miyazaki, and S. Uemura, Context-Aware SVM for Context-Dependent Information Recommendation, 7th International Conference on Mobile Data Management (MDM'06), p.109, 2006.
DOI : 10.1109/MDM.2006.56

P. Paillier, Public-Key Cryptosystems Based on Composite Degree Residuosity Classes, Jacques Stern, editeur, Advances in Cryptology EUROCRYPT '99, pp.223-238, 1999.
DOI : 10.1007/3-540-48910-X_16
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.4035

M. Papadogiorgaki, V. Papastathis, E. Nidelkou, S. Waddington, and B. Bratu, Myriam Ribiere and Ioannis Kompatsiaris. Two-Level Automatic Adaptation of a Distributed User Profile for Personalized News Content Delivery, International Journal of Digital Multimedia Broadcasting, 2008.

]. R. Parameswaran and D. M. Blough, Privacy Preserving Collaborative Filtering Using Data Obfuscation, 2007 IEEE International Conference on Granular Computing (GRC 2007), 2007.
DOI : 10.1109/GrC.2007.133

J. Parra-arnau, D. Rebollo-monedero, and J. Forné, A Privacy-Protecting Architecture for Collaborative Filtering via Forgery and Suppression of Ratings, Lecture Notes in Computer Science, vol.7122, issue.54, pp.42-57, 2012.
DOI : 10.1007/978-3-642-28879-1_4

M. Pazzani and D. Billsus, Learning and Revising User Profiles: The Identification of Interesting Web Sites, Machine Learning, pp.313-331, 1997.

J. Michael and . Pazzani, A Framework for Collaborative, Content-Based and Demographic Filtering, Artif. Intell. Rev, vol.13, issue.5-6, pp.393-408, 1999.

H. Polat and W. Du, SVD-based collaborative filtering with privacy, Proceedings of the 2005 ACM symposium on Applied computing , SAC '05, pp.791-795, 2005.
DOI : 10.1145/1066677.1066860

S. Rendle, C. Freudenthaler, Z. Gantner, and L. S. Thieme, BPR: Bayesian personalized ranking from implicit feedback, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI '09, pp.452-461, 2009.

P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, GroupLens, Proceedings of the 1994 ACM conference on Computer supported cooperative work , CSCW '94, pp.175-186, 1994.
DOI : 10.1145/192844.192905

]. G. Salton, A. Wong, and C. S. Yang, A vector space model for automatic indexing, Communications of the ACM, vol.18, issue.11, pp.613-620, 1975.
DOI : 10.1145/361219.361220

G. Salton and C. Buckley, Term-weighting approaches in automatic text retrieval, Information Processing & Management, vol.24, issue.5, pp.513-523, 1988.
DOI : 10.1016/0306-4573(88)90021-0

B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, Item-based collaborative filtering recommendation algorithms, Proceedings of the tenth international conference on World Wide Web , WWW '01, pp.285-295, 2001.
DOI : 10.1145/371920.372071
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.167.7612

G. Sarwar, J. Karypis, J. Konstan, and . Riedl, Incremental Singular Value Decomposition Algorithms for Highly Scalable Recommender Systems, Fifth International Conference on Computer and Information Science, pp.27-28, 2002.

E. Robert, Y. Schapire, and . Freund, Boosting: Foundations and algorithms (adaptive computation and machine learning series), 2012.

S. Schelter, C. Boden, and V. Markl, Scalable similarity-based neighborhood methods with MapReduce, Proceedings of the sixth ACM conference on Recommender systems, RecSys '12, pp.163-170
DOI : 10.1145/2365952.2365984

S. Schelter, C. Boden, M. Schenck, A. Alexandrov, and V. Markl, Distributed matrix factorization with mapreduce using a series of broadcast-joins, Proceedings of the 7th ACM conference on Recommender systems, RecSys '13, pp.281-284
DOI : 10.1145/2507157.2507195

R. Schifanella, A. Panisson, C. Gena, and G. Ruffo, MobHinter, Proceedings of the 2008 ACM conference on Recommender systems, RecSys '08, pp.27-34, 2008.
DOI : 10.1145/1454008.1454014

S. Guy and A. Gunawardana, Evaluating Recommendation Systems, 2011.

Y. Shi, A. Karatzoglou, L. Baltrunas, M. Larson, N. Oliver et al., CLiMF, Proceedings of the sixth ACM conference on Recommender systems, RecSys '12, pp.139-146, 2012.
DOI : 10.1145/2365952.2365981

R. Shokri, P. Pedarsani, G. Theodorakopoulos, and J. P. Hubaux, Preserving privacy in collaborative filtering through distributed aggregation of offline profiles, Proceedings of the third ACM conference on Recommender systems, RecSys '09, pp.157-164, 2009.
DOI : 10.1145/1639714.1639741

A. Sieg, B. Mobasher, and R. Burke, Web search personalization with ontological user profiles, Proceedings of the sixteenth ACM conference on Conference on information and knowledge management , CIKM '07, pp.525-534, 2007.
DOI : 10.1145/1321440.1321515

]. S. Spiekermann and L. F. Cranor, Engineering Privacy. Software Engineering, IEEE Transactions on, vol.35, issue.1, pp.67-82, 2009.

E. Toch, Y. Wang, and L. Cranor, Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems, User Modeling and User-Adapted Interaction, vol.43, issue.2, pp.203-220, 2012.
DOI : 10.1007/s11257-011-9110-z

D. Tomozei and L. Massoulié, Distributed User Profiling via Spectral Methods, 2011.
DOI : 10.1145/1811039.1811098
URL : https://hal.archives-ouvertes.fr/hal-00976912

]. Vallet, I. Cantador, M. Fernández, and P. Castells, A Multi-Purpose Ontology-Based Approach for Personalized Content Filtering and Retrieval. Semantic Media Adaptation and Personalization, International Workshop on, vol.0, pp.19-24, 2006.

]. V. Vapnik, An overview of statistical learning theory, IEEE Transactions on Neural Networks, vol.10, issue.5, pp.988-999, 1999.
DOI : 10.1109/72.788640

L. Stanley and . Warner, Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias, Journal of the American Statistical Association, vol.60, issue.309, p.63, 1965.

]. Xu, Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent, p.73, 2011.

]. Zhao, X. Feng, J. Li, and B. Liu, Shared collaborative filtering, Proceedings of the fifth ACM conference on Recommender systems, RecSys '11, pp.29-36, 2011.
DOI : 10.1145/2043932.2043942