D. Agrawal and C. C. Aggarwal, On the design and quantification of privacy preserving data mining algorithms, Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems , PODS '01, pp.247-255, 2001.
DOI : 10.1145/375551.375602

S. Mário, M. E. Alvim, K. Andrés, C. Chatzikokolakis, and . Palamidessi, On the relation between differential privacy and quantitative information flow, Int. Conf. on Automata, Languages and Programming, pp.60-76, 2011.

M. Abk-+-13-]-armen-aghasaryan, D. Bouzid, M. Kostadinov, A. Kothari, and . Nandi, On the use of lsh for privacy preserving personalization, 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, pp.362-371, 2013.

P. Sérgio-almeida, C. Baquero, N. Preguia, and D. Hutchison, Scalable Bloom Filters, Information Processing Letters, vol.101, issue.6, pp.255-261, 2007.
DOI : 10.1016/j.ipl.2006.10.007

[. Ailon and B. Chazelle, Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform, STOC '06 -Proceedings of the thirty-eighth annual ACM symposium on Theory of computing, pp.557-563, 2006.
DOI : 10.1145/1132516.1132597

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

D. Achlioptas, Database-friendly random projections, Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems , PODS '01, pp.274-281, 2001.
DOI : 10.1145/375551.375608

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

S. Mohammad-alaggan, A. Gambs, and . Kermarrec, BLIP: Non-interactive Differentially-Private Similarity Computation on Bloom Filters, 14th Int. Symp. on Stabilization, Safety, and Security of Distributed Systems, 2012.

S. Mohammad-alaggan, A. Gambs, and . Kermarrec, Heterogeneous differential privacy. arXiv preprint arXiv:1504.06998, 2015. [AMS96] Noga Alon, Yossi Matias, and Mario Szegedy. The space complexity of approximating the frequency moments Privacy-preserving data mining, STOCAS00] Rakesh Agrawal and Ramakrishnan Srikant, pp.20-29, 1996.

A. Z. Broder, M. Charikar, A. M. Frieze, and M. Mitzenmacher, Min-Wise Independent Permutations, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, STOC '98, pp.327-336, 1998.
DOI : 10.1006/jcss.1999.1690

URL : http://doi.org/10.1006/jcss.1999.1690

A. Berlioz, M. A. Friedman, R. Kaafar, S. Boreli, and . Berkovsky, Applying Differential Privacy to Matrix Factorization, Proceedings of the 9th ACM Conference on Recommender Systems, RecSys '15, pp.107-114, 2015.
DOI : 10.1145/2792838.2800173

S. John, D. Breese, C. Heckerman, and . Kadie, Empirical analysis of predictive algorithms for collaborative filtering, Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp.43-52, 1998.

J. Bennett and S. Lanning, The netflix prize, Proceedings of KDD cup and workshop, p.35, 2007.

H. Burton and . Bloom, Space/time trade-offs in hash coding with allowable errors, Communications of the ACM, vol.13, issue.7, pp.422-426, 1970.

M. Bonomi, R. Mitzenmacher, S. Panigrahy, G. Singh, and . Varghese, An Improved Construction for Counting Bloom Filters, Algorithms?ESA 2006, pp.684-695, 2006.
DOI : 10.1007/11841036_61

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

M. David, . Blei, Y. Andrew, . Ng, I. Michael et al., Latent dirichlet allocation, Journal of machine Learning research, vol.3, pp.993-1022, 2003.

A. Beimel, K. Nissim, and E. Omri, Distributed Private Data Analysis: Simultaneously Solving How and What, Proc. of Advances in Cryptology, pp.451-468, 2008.
DOI : 10.1007/978-3-540-85174-5_25

]. P. Bou12 and . Boufounos, Universal rate-efficient scalar quantization, IEEE Trans. Inform. Theory, vol.58, issue.3, 2012.

D. Billsus, J. Michael, and . Pazzani, Learning collaborative information filters, Icml, pp.46-54, 1998.

[. Bachrach, E. Porat, S. Jeffrey, and . Rosenschein, Sketching techniques for collaborative filtering, IJCAI, pp.2016-2021, 2009.

Z. Andrei and . Broder, On the resemblance and containment of documents, Compression and Complexity of Sequences 1997. Proceedings, pp.21-29, 1997.

M. Charikar, K. Chen, and M. Farach-colton, Finding frequent items in data streams, ICALP, 2002.

]. M. Cha02 and . Charikar, Similarity estimation techniques from rounding algorithms, STOC, pp.380-388, 2002.

. A. Ckn-+-11-]-j, A. Calandrino, A. Kilzer, E. W. Narayanan, V. Felten et al., you might also like:" privacy risks of collaborative filtering, SP, pp.231-246, 2011.

J. Bernard-chazelle, R. Kilian, A. Rubinfeld, and . Tal, The bloomier filter: an efficient data structure for static support lookup tables, Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms, pp.30-39, 2004.

K. Chen and L. Liu, A Survey of Multiplicative Perturbation for Privacy-Preserving Data Mining, Privacy-Preserving Data Mining, pp.157-181, 2008.
DOI : 10.1007/978-0-387-70992-5_7

[. Chaudhuri, C. Monteleoni, and A. D. Sarwate, Differentially private empirical risk minimization, J. Mach. Learn. Res, vol.12, pp.1069-1109, 2011.

G. Cormode, Sketch techniques for approximate query processing, FnTD. NOW publishers, 2011.

M. [. Dong, K. Charikar, and . Li, Asymmetric distance estimation with sketches for similarity search in high-dimensional spaces, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pp.123-130, 2008.
DOI : 10.1145/1390334.1390358

S. Abhinandan, M. Das, A. Datar, S. Garg, and . Rajaram, Google news personalization: scalable online collaborative filtering, WWW, pp.271-280, 2007.

M. Datar, N. Immorlica, P. Indyk, and V. S. Mirrokni, Locality-sensitive hashing scheme based on p-stable distributions, Proceedings of the twentieth annual symposium on Computational geometry , SCG '04, pp.253-262, 2004.
DOI : 10.1145/997817.997857

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

[. Deshpande and G. Karypis, recommendation algorithms, ACM Transactions on Information Systems, vol.22, issue.1, pp.143-177, 2004.
DOI : 10.1145/963770.963776

C. Dwork, K. Kenthapadi, F. Mcsherry, I. Mironov, and M. Naor, Our Data, Ourselves: Privacy Via Distributed Noise Generation, EUROCRYPT, pp.486-503, 2006.
DOI : 10.1007/11761679_29

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

[. Dasgupta, R. Kumar, and T. Sarlós, A sparse Johnson, Proceedings of the 42nd ACM symposium on Theory of computing, STOC '10, pp.341-350, 2010.
DOI : 10.1145/1806689.1806737

A. Dobra, Measuring the disclosure risk for multi-way tables with fixed marginals corresponding to decomposable log-linear models, 2000.

F. Deng and D. Rafiei, Approximately detecting duplicates for streaming data using stable bloom filters, Proceedings of the 2006 ACM SIGMOD international conference on Management of data , SIGMOD '06, pp.25-36, 2006.
DOI : 10.1145/1142473.1142477

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

C. Dwork, N. Guy, and . Rothblum, Concentrated differential priacy. arXiv preprint, 2016.

P. Diaconis and B. Sturmfels, Algebraic algorithms for sampling from conditional distributions. The Annals of Statistics, pp.363-397, 1998.
DOI : 10.1214/aos/1030563990

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

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

C. Dwork, Differential Privacy: A Survey of Results, International Conference on Theory and Applications of Models of Computation, pp.1-19, 2008.
DOI : 10.1007/978-3-540-79228-4_1

[. Fan, P. Cao, J. Almeida, and A. Z. Broder, Summary cache: a scalable wide-area Web cache sharing protocol, IEEE/ACM Transactions on Networking, vol.8, issue.3, pp.281-293, 2000.
DOI : 10.1109/90.851975

A. [. Furon, F. Guyader, and . Cerou, Decoding fingerprints using the Markov Chain Monte Carlo method, 2012 IEEE International Workshop on Information Forensics and Security (WIFS), pp.187-192, 2012.
DOI : 10.1109/WIFS.2012.6412647

H. [. Frankl and . Maehara, The Johnson-Lindenstrauss lemma and the sphericity of some graphs, Journal of Combinatorial Theory, Series B, vol.44, issue.3, pp.355-362, 1987.
DOI : 10.1016/0095-8956(88)90043-3

A. Friedman and A. Schuster, Data mining with differential privacy, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pp.493-502, 2010.
DOI : 10.1145/1835804.1835868

]. J. Fuc11 and . Fuchs, Spread representations, ASILOMAR, 2011.

S. [. Gong and . Lazebnik, Iterative quantization: A procrustean approach to learning binary codes, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995432

K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, Eigentaste: A constant time collaborative filtering algorithm, Information Retrieval, vol.4, issue.2, pp.133-151, 2001.
DOI : 10.1023/A:1011419012209

M. [. Goyal, N. T. Vetterli, and . Thao, Quantized overcomplete expansions in IR/sup N/: analysis, synthesis, and algorithms, IEEE Transactions on Information Theory, vol.44, issue.1, pp.16-31, 1998.
DOI : 10.1109/18.650985

[. Guo and X. Wu, On the use of spectral filtering for privacy preserving data mining, Proceedings of the 2006 ACM symposium on Applied computing , SAC '06, pp.622-626, 2006.
DOI : 10.1145/1141277.1141418

[. Guo and X. Wu, Deriving Private Information from Arbitrarily Projected Data, Advances in Knowledge Discovery and Data Mining, pp.84-95, 2007.
DOI : 10.1007/978-3-540-71701-0_11

Z. Huang, W. Du, and B. Chen, Deriving private information from randomized data, Proceedings of the 2005 ACM SIGMOD international conference on Management of data , SIGMOD '05, pp.37-48, 2005.
DOI : 10.1145/1066157.1066163

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

[. Hofmann and D. Hartmann, Collaborative filtering with privacy via factor analysis, Proceedings of the 2005 ACM symposium on applied computing, pp.791-795, 2005.

L. Hill, M. Stead, G. Rosenstein, and . Furnas, Recommending and evaluating choices in a virtual community of use, Proceedings of the SIGCHI conference on Human factors in computing systems, CHI '95, pp.194-201, 1995.
DOI : 10.1145/223904.223929

P. Indyk and R. Motwani, Approximate nearest neighbors, Proceedings of the thirtieth annual ACM symposium on Theory of computing , STOC '98, pp.604-613, 1998.
DOI : 10.1145/276698.276876

M. [. Jégou, C. Douze, and . Schmid, Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search, ECCV, 2008.
DOI : 10.1007/978-3-540-88682-2_24

]. H. Jds11a, M. Jégou, C. Douze, and . Schmid, Product quantization for nearest neighbor search, IEEE Trans. PAMI, vol.33, issue.1, pp.117-128, 2011.

H. Jegou, M. Douze, and C. Schmid, Product Quantization for Nearest Neighbor Search, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.1, pp.117-128, 2011.
DOI : 10.1109/TPAMI.2010.57

URL : https://hal.archives-ouvertes.fr/inria-00514462

H. Jégou, M. Douze, C. Schmid, and P. Pérez, Aggregating local descriptors into a compact image representation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540039

T. [. Jégou, J. J. Furon, and . Fuchs, Anti-sparse coding for approximate nearest neighbor search, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012.
DOI : 10.1109/ICASSP.2012.6288307

H. [. Jain, P. Jégou, and . Gros, Asymmetric hamming embedding, Proceedings of the 19th ACM international conference on Multimedia, MM '11, 2011.
DOI : 10.1145/2072298.2072035

URL : https://hal.archives-ouvertes.fr/inria-00607278

[. Jain, P. Kothari, and A. Thakurta, Differentially private online learning, 2011.

B. William, J. Johnson, and . Lindenstrauss, Extensions of lipschitz mappings into a hilbert space Differential privacy and machine learning: a survey and review, Contemporary mathematics, vol.26, issue.1, pp.189-206, 1984.

J. J. Ji, S. Li, B. Yan, Q. Zhang, and . Tian, Super-bit locality-sensitive hashing, NIPS, 2012.

R. [. Jégou, M. Tavenard, L. Douze, and . Amsaleg, Searching in one billion vectors: Re-rank with source coding, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011.
DOI : 10.1109/ICASSP.2011.5946540

[. Koren, R. Bell, and C. Volinsky, Matrix Factorization Techniques for Recommender Systems, Computer, vol.42, issue.8, pp.30-37, 2009.
DOI : 10.1109/MC.2009.263

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

]. B. Kd09a, T. Kulis, and . Darrell, Learning to hash with binary reconstructive embeddings, NIPS, 2009.

B. Kulis and T. Darrell, Learning to hash with binary reconstructive embeddings, Advances in neural information processing systems, pp.1042-1050, 2009.

[. Kenthapadi, A. Korolova, I. Mironov, and N. Mishra, Privacy via the johnson-lindenstrauss transform, 2012.

. Kln-+-11-]-shiva-prasad-kasiviswanathan, K. Homin, K. Lee, S. Nissim, A. Raskhodnikova et al., What Can We Learn Privately?, SIAM Journal on Computing, vol.40, issue.3, pp.793-826, 2011.
DOI : 10.1137/090756090

M. Daniel, J. Kane, and . Nelson, A derandomized sparse johnsonlindenstrauss transform. arXiv preprint, 2010.

M. Daniel, J. Kane, and . Nelson, Sparser johnson-lindenstrauss transforms, Journal of the ACM (JACM), vol.61, issue.1, p.4, 2014.

[. Knill, D. Schliep, and . Torney, Interpretation of Pooling Experiments Using the Markov Chain Monte Carlo Method, Journal of Computational Biology, vol.3, issue.3, pp.395-406, 1996.
DOI : 10.1089/cmb.1996.3.395

M. Kapralov and K. Talwar, On differentially private low rank approximation, In ACM-SIAM, pp.1395-1414, 2013.
DOI : 10.1137/1.9781611973105.101

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

[. Karatzoglou, M. Weimer, and A. J. Smola, Collaborative filtering on a budget, AISTATS, 2010.

[. Liberty, N. Ailon, and A. Singer, Dense fast random projections and lean walsh transforms, Approximation, Randomization and Combinatorial Optimization. Algorithms and Techniques, pp.512-522, 2008.
DOI : 10.1007/978-3-540-85363-3_40

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

[. Lee and C. Clifton, How Much Is Enough? Choosing ?? for Differential Privacy, Information Security, pp.325-340, 2011.
DOI : 10.1109/TKDE.2009.125

[. Li, W. Kenneth, T. J. Church, and . Hastie, Conditional random sampling: A sketch-based sampling technique for sparse data, Advances in neural information processing systems, pp.873-880, 2006.

K. Liu, C. Giannella, and H. Kargupta, A Survey of Attack Techniques on Privacy-Preserving Data Perturbation Methods, Privacy- Preserving Data Mining of Advances in Database Systems, pp.359-381, 2008.
DOI : 10.1007/978-0-387-70992-5_15

[. Liu, J. He, C. Deng, and B. Lang, Collaborative Hashing, 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.2139-2146, 2014.
DOI : 10.1109/CVPR.2014.275

[. Li and A. C. König, -bit minwise hashing, Communications of the ACM, vol.54, issue.8, pp.101-109, 2011.
DOI : 10.1145/1978542.1978566

URL : https://hal.archives-ouvertes.fr/hal-00358055

]. D. Low04 and . Lowe, Distinctive image features from scale-invariant keypoints, IJCV, vol.60, issue.2, pp.91-110, 2004.

[. Li, A. Shrivastava, L. Joshua, . Moore, C. Arnd et al., Hashing algorithms for large-scale learning Amazon. com recommendations: Item-to-item collaborative filtering, NIPS, pp.2672-268076, 2003.

Z. Liu, Y. Wang, and A. Smola, Fast Differentially Private Matrix Factorization, Proceedings of the 9th ACM Conference on Recommender Systems, RecSys '15, pp.171-178, 2015.
DOI : 10.1145/2792838.2800191

URL : http://arxiv.org/abs/1505.01419

Y. D. Li, Z. Zhang, M. Winslett, and Y. Yang, Compressive mechanism, Proceedings of the 10th annual ACM workshop on Privacy in the electronic society, WPES '11, 2011.
DOI : 10.1145/2046556.2046581

J. Matousek, On variants of the johnson?lindenstrauss lemma. Random Structures & Algorithms, pp.142-156, 2008.

L. Melis, G. Danezis, and E. Cristofaro, Efficient private statistics with succinct sketches. arXiv preprint arXiv:1508.06110, 2015. [MF11] P. Meerwald and T. Furon. Group testing meets traitor tracing, Acoustics , Speech and Signal Processing (ICASSP) IEEE International Conference on, pp.4204-4207, 2011.

T. [. Meerwald and . Furon, Toward Practical Joint Decoding of Binary Tardos Fingerprinting Codes, IEEE Transactions on Information Forensics and Security, vol.7, issue.4, pp.1168-1180, 2012.
DOI : 10.1109/TIFS.2012.2195655

F. Mcsherry and I. Mironov, Differentially private recommender systems, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.627-636, 2009.
DOI : 10.1145/1557019.1557090

P. Moulin, Universal fingerprinting: Capacity and random-coding exponents, 2008 IEEE International Symposium on Information Theory, 2008.
DOI : 10.1109/ISIT.2008.4594980

URL : http://arxiv.org/abs/0801.3837

J. Matousek and M. Stojakovi?, On restricted min-wise independence of permutations. Random Structures & Algorithms Privacy via pseudorandom sketches, MS06] Nina Mishra and Mark Sandler ACM SIGMOD-SIGACT-SIGART, pp.397-408, 2003.

A. Mnih and R. Salakhutdinov, Probabilistic matrix factorization, Advances in neural information processing systems, pp.1257-1264, 2007.

K. [. Mcsherry and . Talwar, Mechanism Design via Differential Privacy, 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07), pp.94-103, 2007.
DOI : 10.1109/FOCS.2007.66

[. Muthukrishnan, Data Streams: Algorithms and Applications, Foundations and Trends?? in Theoretical Computer Science, vol.1, issue.2, 2005.
DOI : 10.1561/0400000002

URL : http://ce.sharif.edu/courses/90-91/1/ce797-1/resources/root/Data_Streams_-_Algorithms_and_Applications.pdf

[. Nandi, A. Aghasaryan, and M. Bouzid, P3: A privacy preserving personalization middleware for recommendation-based services, Hot Topics in Privacy Enhancing Technologies Symposium, 2011.

M. Norouzi, M. David, and . Blei, Minimal loss hashing for compact binary codes, Proceedings of the 28th international conference on machine learning (ICML-11), pp.353-360, 2011.

K. Nissim, S. Raskhodnikova, and A. Smith, Smooth sensitivity and sampling in private data analysis, Proceedings of the thirty-ninth annual ACM symposium on Theory of computing , STOC '07, pp.75-84, 2007.
DOI : 10.1145/1250790.1250803

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

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

[. Paulevé, H. Jégou, and L. Amsaleg, Locality sensitive hashing: A comparison of hash function types and querying mechanisms, Pattern Recognition Letters, vol.31, issue.11, pp.311348-1358, 2010.
DOI : 10.1016/j.patrec.2010.04.004

F. Perronnin, Y. Liu, J. Sanchez, and H. Poirier, Large-scale image retrieval with compressed Fisher vectors, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.
DOI : 10.1109/CVPR.2010.5540009

[. Pham and R. Pagh, Fast and scalable polynomial kernels via explicit feature maps, Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '13, pp.239-247, 2013.
DOI : 10.1145/2487575.2487591

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

[. Putze, P. Sanders, and J. Singler, Cache-, hash-and space-efficient bloom filters, Experimental Algorithms, pp.108-121, 2007.
DOI : 10.1007/978-3-540-72845-0_9

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

G. [. Robert and . Casella, Monte Carlo statistical methods, 2004.

C. Steffen-rendle, Z. Freudenthaler, L. Gantner, and . Schmidt-thieme, Bpr: Bayesian personalized ranking from implicit feedback, Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence, pp.452-461, 2009.

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

[. Rottenstreich, Y. Kanizo, and I. Keslassy, The Variable-Increment Counting Bloom Filter, IEEE/ACM Transactions on Networking, vol.22, issue.4, pp.1092-1105, 2014.
DOI : 10.1109/TNET.2013.2272604

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

S. [. Raginsky and . Lazebnik, Locality-sensitive binary codes from shiftinvariant kernels, NIPS, 2010.

]. A. Rr07a, B. Rahimi, and . Recht, Random features for large-scale kernel machines, NIPS, 2007.

A. Rahimi and B. Recht, Random features for large-scale kernel machines, Advances in neural information processing systems, pp.1177-1184, 2007.

D. Jasson, N. Rennie, and . Srebro, Fast maximum margin matrix factorization for collaborative prediction, Proceedings of the 22nd international conference on Machine learning, pp.713-719, 2005.

K. [. Sarwate and . Chaudhuri, Signal Processing and Machine Learning with Differential Privacy: Algorithms and Challenges for Continuous Data, IEEE Signal Processing Magazine, vol.30, issue.5, pp.86-94, 2013.
DOI : 10.1109/MSP.2013.2259911

[. Salakhutdinov and G. Hinton, Semantic hashing, International Journal of Approximate Reasoning, vol.50, issue.7, pp.969-978, 2009.
DOI : 10.1016/j.ijar.2008.11.006

URL : http://doi.org/10.1016/j.ijar.2008.11.006

. Srivastava, E. Geoffrey, A. Hinton, I. Krizhevsky, R. Sutskever et al., Dropout: a simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

T. Sj-+-03-]-nathan-srebro and . Jaakkola, Weighted low-rank approximations, ICML, pp.720-727, 2003.

O. [. Sejdinovic and . Johnson, Note on noisy group testing: Asymptotic bounds and belief propagation reconstruction, 2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2010.
DOI : 10.1109/ALLERTON.2010.5707018

URL : http://arxiv.org/abs/1010.2441

G. Sarwar, J. Karypis, J. Konstan, and . Riedl, Application of dimensionality reduction in recommender system-a case study, 2000.

G. Sarwar, J. Karypis, J. Konstan, and . Riedl, Itembased collaborative filtering recommendation algorithms, Proceedings of the 10th international conference on World Wide Web, pp.285-295, 2001.

[. Slaney, Optimal Parameters for Locality-Sensitive Hashing, Proceedings of the IEEE, vol.100, issue.9, pp.2604-2623, 2012.
DOI : 10.1109/JPROC.2012.2193849

[. Shardanand and P. Maes, Social information filtering, Proceedings of the SIGCHI conference on Human factors in computing systems, CHI '95, pp.210-217, 1995.
DOI : 10.1145/223904.223931

. Sm08a-]-ruslan, A. Salakhutdinov, and . Mnih, Bayesian probabilistic matrix factorization using Markov chain Monte Carlo, Proceedings of the 25th international conference on Machine learning -ICML '08, pp.880-887, 2008.

. Sm08b-]-ruslan, A. Salakhutdinov, G. Mnih, J. Hinton-nathan-srebro, T. S. Rennie et al., Probabilistic matrix factorization Restricted boltzmann machines for collaborative filtering Maximum-margin matrix factorization, Advances in Neural Information Processing SystemsSMH07] Ruslan Salakhutdinov, Andriy Mnih, Proceedings of the 24th international conference on Machine learning Advances in neural information processing systems, pp.791-798, 2004.

A. [. Simonyan, A. Vedaldi, and . Zisserman, Learning Local Feature Descriptors Using Convex Optimisation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.36, issue.8, 2013.
DOI : 10.1109/TPAMI.2014.2301163

]. A. Tfw08a, R. Torralba, Y. Fergus, and . Weiss, Small codes and large databases for recognition, CVPR, 2008.

A. Torralba, R. Fergus, and Y. Weiss, Small codes and large image databases for recognition, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587633

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

H. Lyle, . Ungar, P. Dean, and . Foster, Clustering methods for collaborative filtering, AAAI workshop on recommendation systems, pp.114-129, 1998.

K. [. Vollmer, Y. W. Zygalakis, and . Teh, (non-) asymptotic properties of stochastic gradient langevin dynamics, 2015.

L. Stanley and . Warner, Randomized response: A survey technique for eliminating evasive answer bias, Journal of the American Statistical Association, vol.60, issue.309, pp.63-69, 1965.

A. Wdl-+-09-]-kilian-weinberger, J. Dasgupta, A. Langford, J. Smola, and . Attenberg, Feature hashing for large scale multitask learning, 2009.

[. Wang, E. Stephen, A. Fienberg, and . Smola, Privacy for free: Posterior sampling and stochastic gradient monte carlo, 2015.

[. Weimer, A. Karatzoglou, Q. Viet-le, and A. Smola, Maximum margin matrix factorization for collaborative ranking Advances in neural information processing systems, pp.1-8, 2007.

[. Weimer, A. Karatzoglou, and A. Smola, Improving maximum margin matrix factorization, Machine Learning, pp.263-276, 2008.
DOI : 10.1007/978-3-540-87479-9_12

URL : https://hal.archives-ouvertes.fr/hal-00482747

O. Williams and F. Mcsherry, Probabilistic inference and differential privacy, Advances in Neural Information Processing Systems, pp.2451-2459, 2010.

M. Welling and Y. W. Teh, Bayesian learning via stochastic gradient langevin dynamics, ICML, pp.681-688, 2011.

]. Y. Wtf09a, A. Weiss, R. Torralba, and . Fergus, Spectral hashing, NIPS, 2009.

[. Weiss, A. Torralba, and R. Fergus, Spectral hashing, Advances in neural information processing systems, pp.1753-1760, 2009.

H. Wang, N. Wang, and D. Yeung, Collaborative Deep Learning for Recommender Systems, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, pp.1235-1244, 2015.
DOI : 10.1145/2783258.2783273

URL : http://arxiv.org/abs/1409.2944

K. Zhou and H. Zha, Learning binary codes for collaborative filtering, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '12, pp.498-506, 2012.
DOI : 10.1145/2339530.2339611

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=