S. Banerjee, N. Hegde, and L. Massoulie, The price of privacy in untrusted recommender systems. Selected Topics in Signal Processing, IEEE Journal, issue.97, pp.1319-1331, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01226756

V. D. Blondel, J. Guillaume, R. Lambiotte, and E. Lefebvre, Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, vol.2008, issue.10, p.10008, 2008.
DOI : 10.1088/1742-5468/2008/10/P10008

URL : https://hal.archives-ouvertes.fr/hal-01146070

K. Chaudhuri, A. D. Sarwate, and K. Sinha, A near-optimal algorithm for differentially-private principal components, The Journal of Machine Learning Research, vol.14, issue.1, pp.2905-2943, 2013.

R. Chen, B. C. Fung, P. S. Yu, and B. C. Desai, Correlated network data publication via differential privacy, The VLDB Journal, vol.2, issue.1, pp.653-676, 2014.
DOI : 10.1007/s00778-013-0344-8

A. Clauset, C. Moore, and M. E. Newman, Hierarchical structure and the prediction of missing links in networks, Nature, vol.104, issue.7191, pp.98-101, 2008.
DOI : 10.1038/nature06830

A. Clauset, M. E. Newman, and C. Moore, Finding community structure in very large networks, Physical Review E, vol.70, issue.6, p.66111, 2004.
DOI : 10.1103/PhysRevE.70.066111

G. Cormode, C. Procopiuc, D. Srivastava, and T. T. Tran, Differentially private summaries for sparse data, Proceedings of the 15th International Conference on Database Theory, ICDT '12, pp.299-311, 2012.
DOI : 10.1145/2274576.2274608

C. Dwork, F. Mcsherry, K. Nissim, and A. Smith, Calibrating Noise to Sensitivity in Private Data Analysis, TCC, pp.265-284, 2006.
DOI : 10.1007/11681878_14

C. Dwork and A. Roth, The Algorithmic Foundations of Differential Privacy, Foundations and Trends?? in Theoretical Computer Science, vol.9, issue.3-4, pp.211-407, 2014.
DOI : 10.1561/0400000042

S. Fortunato, Community detection in graphs, Physics Reports, vol.486, issue.3-5, pp.75-174, 2010.
DOI : 10.1016/j.physrep.2009.11.002

A. Ghosh, T. Roughgarden, and M. Sundararajan, Universally Utility-maximizing Privacy Mechanisms, SIAM Journal on Computing, vol.41, issue.6, pp.1673-1693, 2012.
DOI : 10.1137/09076828X

URL : http://arxiv.org/abs/0811.2841

A. Gupta, A. Roth, and J. Ullman, Iterative Constructions and Private Data Release, Theory of Cryptography, pp.339-356, 2012.
DOI : 10.1007/978-3-642-28914-9_19

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

F. Mcsherry and K. 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

F. D. Mcsherry, Privacy integrated queries: an extensible platform for privacy-preserving data analysis, SIGMOD, pp.19-30, 2009.

A. Medus, G. Acuna, and C. Dorso, Detection of community structures in networks via global optimization. Physica A: Statistical Mechanics and its Applications, pp.593-604, 2005.

Y. Mülle, C. Clifton, and K. Böhm, Privacy-integrated graph clustering through differential privacy, 2015.

M. E. Newman and M. Girvan, Finding and evaluating community structure in networks, Physical Review E, vol.69, issue.2, p.26113, 2004.
DOI : 10.1103/PhysRevE.69.026113

H. H. Nguyen, A. Imine, and M. Rusinowitch, Differentially Private Publication of Social Graphs at Linear Cost, Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ASONAM '15, 2015.
DOI : 10.1145/2808797.2809385

URL : https://hal.archives-ouvertes.fr/hal-01179528

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

P. Pons and M. Latapy, Computing communities in large networks using random walks, Computer and Information Sciences-ISCIS 2005, pp.284-293, 2005.

A. Prat-pérez, D. Dominguez-sal, and J. Larriba-pey, High quality, scalable and parallel community detection for large real graphs, Proceedings of the 23rd international conference on World wide web, WWW '14, pp.225-236, 2014.
DOI : 10.1145/2566486.2568010

U. N. Raghavan, R. Albert, and S. Kumara, Near linear time algorithm to detect community structures in large-scale networks, Physical Review E, vol.76, issue.3, p.36106, 2007.
DOI : 10.1103/PhysRevE.76.036106

J. Reichardt and S. Bornholdt, Statistical mechanics of community detection, Physical Review E, vol.74, issue.1, p.16110, 2006.
DOI : 10.1103/PhysRevE.74.016110

M. Rosvall and C. T. Bergstrom, Maps of random walks on complex networks reveal community structure, Proceedings of the National Academy of Sciences, pp.1118-1123, 2008.
DOI : 10.1073/pnas.0706851105

D. Su, J. Cao, N. Li, E. Bertino, and H. Jin, Differentially private k-means clustering. arXiv preprint, 2015.
DOI : 10.1145/2857705.2857708

URL : http://arxiv.org/abs/1504.05998

Y. Wang and X. Wu, Preserving differential privacy in degree-correlation based graph generation, TDP, vol.6, issue.2, p.127, 2013.

Q. Xiao, R. Chen, and K. Tan, Differentially private network data release via structural inference, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '14, pp.911-920, 2014.
DOI : 10.1145/2623330.2623642

J. Yang and J. Leskovec, Defining and evaluating network communities based on ground-truth, ICDM, pp.745-754, 2012.

J. Zhang, G. Cormode, C. M. Procopiuc, D. Srivastava, and X. Xiao, Private Release of Graph Statistics using Ladder Functions, Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD '15, pp.731-745, 2015.
DOI : 10.1145/2723372.2737785