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Beyond personalization and anonymity: towards a group-based recommender system

Published: 24 March 2014 Publication History

Abstract

Recommender systems have received considerable attention in recent years. Yet with the development of information technology and social media, the risk in revealing private data to service providers has been a growing concern to more and more users. Trade-offs between quality and privacy in recommender systems naturally arise. In this paper, we present a privacy preserving recommendation framework based on groups. The main idea is to use groups as a natural middleware to preserve users' privacy. A distributed preference exchange algorithm is proposed to ensure the anonymity of data, wherein the effective size of the anonymity set asymptotically approaches the group size with time. We construct a hybrid collaborative filtering model based on Markov random walks to provide recommendations and predictions to group members. Experimental results on the MovieLens dataset show that our proposed methods outperform the baseline methods, L+ and Item-Rank, two state-of-the-art personalized recommendation algorithms, for both recommendation precision and hit rate despite the absence of personal preference information.

References

[1]
E. Aïmeur, G. Brassard, J. M. Fernandez, and F. S. M. Onana. Alambic: a privacy-preserving recommender system for electronic commerce. International Journal of Information Security, 7(5): 307--334, 2008.
[2]
L. Baltrunas, T. Makcinskas, and F. Ricci. Group recommendations with rank aggregation and collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems, pages 119--126. ACM, 2010.
[3]
S. Berkovsky and J. Freyne. Group-based recipe recommendations: analysis of data aggregation strategies. In Proceedings of the fourth ACM conference on Recommender systems, pages 111--118. ACM, 2010.
[4]
S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah. Randomized gossip algorithms. IEEE Transactions on Information Theory, pages 2508--2530, 2006.
[5]
S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. In Seventh International World-Wide Web Conference (WWW 1998), 1998.
[6]
J. Canny. Collaborative filtering with privacy via factor analysis. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '02, pages 238--245, New York, NY, USA, 2002. ACM.
[7]
D. Chaum. The dining cryptographers problem: Unconditional sender and recipient untraceability. Journal of cryptology, 1(1): 65--75, 1988.
[8]
C. Dwork. Differential privacy. In Automata, languages and programming, pages 1--12. Springer, 2006.
[9]
F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens. Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. Knowledge and Data Engineering IEEE Transactions, 19(3): 355--369, March 2007.
[10]
M. Gori and A. Pucci. Itemrank: a random-walk based scoring algorithm for recommender engines. In Proceedings of the 20th international joint conference on Artifical intelligence, 2007.
[11]
J. He and W. W. Chu. A social network-based recommender system (SNRS). Springer, 2010.
[12]
M. Kendall. A new measure of rank correlation. Biometrika, pages 81--89, 1932.
[13]
A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam. l-diversity: Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1): 3, 2007.
[14]
A. Machanavajjhala, A. Korolova, and A. D. Sarma. Personalized social recommendations - accurate or private? In Proceedings of the VLDB Endowment, 2011.
[15]
F. McSherry and I. Mironov. Differentially private recommender systems: building privacy into the netflix prize contenders. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009.
[16]
A. Nandi, A. Aghasaryan, and M. Bouzid. P3: A privacy preserving personalization middleware for recommendation based services. In 4th Hot Topics in Privacy Enhancing Technologies, 2011.
[17]
A. Pfitzmann and M. Köhntopp. Anonymity, unobservability, and pseudonymity --- a proposal for terminology. In International workshop on Designing privacy enhancing technologies: design issues in anonymity and unobservability, 2001.
[18]
T. T. Project. Tor Project: Core People, Retrieved 17 July 2008.
[19]
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, 1994.
[20]
A. Serjantov and G. Danezis. Towards an information theoretic metric for anonymity. In PET'02 Proceedings of the 2nd international conference on Privacy enhancing technologies, pages 41--53, 2002.
[21]
S. Shang, Y. Hui, P. Hui, P. Cuff, and S. Kulkarni. Privacy perserving recommendation system based on groups. http://arxiv.org/abs/1305.0540.
[22]
S. Shang, S. Kulkarni, P. Cuff, and P. Hui. A random walk based model incorporating social information for recommendations. 2012 IEEE Machine Learning for Signal Processing Workshop (MLSP), 2012.
[23]
G. Simondon. L'invention dans les techniques. In Cours et conférences, 2005.
[24]
G. Stringhini, C. Kruegel, and G. Vigna. Detecting spammers on social networks. In Proceedings of the 26th Annual Computer Security Applications Conference, pages 1--9. ACM, 2010.
[25]
L. Sweeney. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05): 557--570, 2002.
[26]
K. H. L. Tso-Sutter, L. B. Marinho, and L. Schmidt-Thieme. Tag-aware recommender systems by fusion of collaborative filtering algorithms. Proceedings of the 2008 ACM symposium on Applied computing, 2008.
[27]
S. Vucetic and Z. Obradovic. Collaborative filtering using a regression-based approach. Knowledge and Information Systems, 7: 1--22, 2005.
[28]
H. Young and A. Levenglick. A consistent extension of Condorcet's election principle. SIAM Journal on Applied Mathematics, 35(2): 285--300, 1978.
[29]
H. Yu, M. Kaminsky, P. Gibbons, and A. Flaxman. Sybilguard: defending against sybil attacks via social networks. ACM SIGCOMM Computer Communication Review, 36(4): 267--278, 2006.

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cover image ACM Conferences
SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
March 2014
1890 pages
ISBN:9781450324694
DOI:10.1145/2554850
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 24 March 2014

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Author Tags

  1. group-based social networks
  2. privacy
  3. recommender system

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SAC 2014
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SAC 2014: Symposium on Applied Computing
March 24 - 28, 2014
Gyeongju, Republic of Korea

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SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing
March 31 - April 4, 2025
Catania , Italy

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  • (2024)Influence maximization algorithm based on group trust and local topology structureNeurocomputing10.1016/j.neucom.2023.126936564(126936)Online publication date: Jan-2024
  • (2022)A Survey on Learning Path RecommendationComputer Supported Cooperative Work and Social Computing10.1007/978-981-19-4546-5_45(577-589)Online publication date: 20-Jul-2022
  • (2021)An Era of Recommendation Technologies in IoT: Categorisation by techniques, Challenges and Future ScopePertanika Journal of Science and Technology10.47836/pjst.29.4.0729:4Online publication date: 29-Oct-2021
  • (2021)The Protection of User Preference Privacy in Personalized Information Retrieval: Challenges and OverviewsLibri10.1515/libri-2019-014071:3(227-237)Online publication date: 28-Apr-2021
  • (2019)Differentially-Private and Trustworthy Online Social Multimedia Big Data Retrieval in Edge ComputingIEEE Transactions on Multimedia10.1109/TMM.2018.288550921:3(539-554)Online publication date: Mar-2019
  • (2019)A Privacy-Preserving Distributed Contextual Federated Online Learning Framework with Big Data Support in Social Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2936565(1-1)Online publication date: 2019
  • (2018)Contributive ResearchProceedings of the 2nd International Conference on Web Studies10.1145/3240431.3240435(19-23)Online publication date: 3-Oct-2018
  • (2018)Covering the Sensitive Subjects to Protect Personal Privacy in Personalized RecommendationIEEE Transactions on Services Computing10.1109/TSC.2016.257582511:3(493-506)Online publication date: 1-May-2018
  • (2018)An approach for the protection of users’ book browsing preference privacy in a digital libraryThe Electronic Library10.1108/EL-07-2017-0162Online publication date: 30-Oct-2018
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