- [1]
A. Aghasaryan, M. Bouzid, D. Kostadinov, M. Kothari, and A. Nandi, “On the Use of LSH for Privacy Preserving Personalization,” Proc. 12th IEEE Internat. Conf. on Trust, Security and Privacy in Comput. and Commun. (IEEE TrustCom '13) (Melbourne, Aus., 2013), pp. 362–371.

- [2]
M. S. Charikar, “Similarity Estimation Techniques from Rounding Algorithms,” Proc. 34th ACM Symp. on Theory of Comput. (STOC '02 ) (Montreal, Que., Can., 2002), pp. 380–388.

- [3]
F. Dabek, B. Zhao, P. Druschel, J. Kubiatowicz, and I. Stoica, “Towards a Common API for Structured Peer-to-Peer Overlays,” Proc. 2nd Internat. Workshop on Peer-to-Peer Syst. (IPTPS '03) (Berkeley, CA, 2003).

- [4]
A. Das, M. Datar, A. Garg, and S. Rajaram, “Google News Personalization: Scalable Online Collaborative Filtering,” Proc. 16th Internat. World Wide Web Conf. (WWW '07) (Banff, AB, Can., 2007), pp. 271–280.

- [5]
R. Dingledine, N. Mathewson, and P. Syverson, “Tor: The Second-Generation Onion Router,” Proc. 13th USENIX Security Symp. (SSYM '04) (San Diego, CA, 2004).

- [6]
W. Dong, Z. Wang, W. Josephson, M. Charikar, and K. Li, “Modeling LSH for Performance Tuning,” Proc. 17th ACM Conf. on Inform. and Knowledge Management (CIKM '08) (Napa Valley, CA, 2008), pp. 669–678.

- [7]
M. Fredrikson and B. Livshits, “RePriv: Re-Imagining Content Personalization and In-Browser Privacy,” Proc. IEEE Symp. on Security and Privacy (SP '11) (Berkeley/Oakland, CA, 2011), pp. 131–146.

- [8]
A. Gionis, P. Indyk, and R. Motwani, “Similarity Search in High Dimensions via Hashing,” Proc. 25th Internat. Conf. on Very Large Data Bases (VLDB '99) (Edinburgh, Scot., 1999), pp. 518–529.

- [9]
GroupLens Research, “MovieLens Data Sets,” <http://www.grouplens.org/node/73>. - [10]
S. Guha, B. Cheng, and P. Francis, “Privad: Practical Privacy in Online Advertising,” Proc. 8th USENIX Symp. on Networked Syst. Design and Implementation (NSDI '11) (Boston, MA, 2011).

- [11]
J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, “An Algorithmic Framework for Performing Collaborative Filtering,” Proc. 22nd Internat. ACM SIGIR Conf. on Res. and Dev. in Inform. Retrieval (SIGIR '99) (Berkeley, CA, 1999), pp. 230–237.

- [12]
Y. Koren, “Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model,” Proc. 14th ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining (KDD '08) (Las Vegas, NV, 2008), pp. 426–434.

- [13]
A. Nandi, A. Aghasaryan, and M. Bouzid, “P3: A Privacy Preserving Personalization Middleware for Recommendation-Based Services,” Proc. 4th Hot Topics in Privacy Enhancing Technol. Symp. (HotPETS '11) (Waterloo, Ont., Can., 2011).

- [14]
L. Paulevé, H. Jégou, and L. Amsaleg, “Locality Sensitive Hashing: A Comparison of Hash Function Types and Querying Mechanisms,” Pattern Recognition Lett., 31:11 (2010), 1348–1358. - [15]
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. ACM Conf. on Comput. Supported Cooperative Work (CSCW '94) (Chapel Hill, NC, 1994), pp. 175–186.

- [16]
C. Shahabi, F. Banaei-Kashani, Y.-S. Chen, and D. McLeod, “Yoda: An Accurate and Scalable Web-Based Recommendation System,” Proc. 9th Internat. Conf. on Cooperative Inform. Syst. (CoopIS '01) (Trento, Ita., 2001), pp. 418–432.

- [17]
M. Slaney, Y. Lifshits, and J. He, “Optimal Parameters for Locality-Sensitive Hashing,” Proc. IEEE, 100:9 (2012), 2604–2623. - [18]
H. Steck, “Training and Testing of Recommender Systems on Data Missing Not at Random,” Proc. 16th ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining (KDD '10) (Washington, DC, 2010), pp. 713–722.

- [19]
V. Toubiana, A. Narayanan, D. Boneh, H. Nissenbaum, and S. Barocas, “Adnostic: Privacy Preserving Targeted Advertising,” Proc. Network and Distrib. Syst. Security Symp. (NDSS '10) (San Diego, CA, 2010).