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Differential Privacy for Collaborative Filtering Recommender Algorithm

Published: 11 March 2016 Publication History

Abstract

Collaborative filtering plays an essential role in a recommender system, which recommends a list of items to a user by learning behavior patterns from user rating matrix. However, if an attacker has some auxiliary knowledge about a user purchase history, he/she can infer more information about this user. This brings great threats to user privacy. Some methods adopt differential privacy algorithms in collaborative filtering by adding noises to a rating matrix. Although they provide theoretically private results, the influence on recommendation accuracy are not discussed. In this paper, we solve the privacy problem in recommender system in a different way by applying the differential privacy method into the procedure of recommendation. We design two differentially private recommender algorithms with sampling, named Differentially Private Item Based Recommendation with sampling (DP-IR for short) and Differentially Private User Based Recommendation with sampling(DP-UR for short). Both algorithms are based on the exponential mechanism with a carefully designed quality function. Theoretical analyses on privacy of these algorithms are presented. We also investigate the accuracy of the proposed method and give theoretical results. Experiments are performed on real datasets to verify our methods.

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Cited By

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  • (2024)ID-SR: Privacy-Preserving Social Recommendation Based on Infinite Divisibility for Trustworthy AIACM Transactions on Knowledge Discovery from Data10.1145/363941218:7(1-25)Online publication date: 2-Jan-2024
  • (2024)FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley valueComplex & Intelligent Systems10.1007/s40747-024-01758-911:1Online publication date: 30-Dec-2024
  • (2023)Personalized Privacy Protection-Preserving Collaborative Filtering Algorithm for Recommendation SystemsApplied Sciences10.3390/app1307460013:7(4600)Online publication date: 5-Apr-2023
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cover image ACM Conferences
IWSPA '16: Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics
March 2016
76 pages
ISBN:9781450340779
DOI:10.1145/2875475
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 ACM 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: 11 March 2016

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Author Tags

  1. collaborative filtering
  2. differential privacy
  3. inference attack
  4. recommendation

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CODASPY'16
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IWSPA '16 Paper Acceptance Rate 6 of 20 submissions, 30%;
Overall Acceptance Rate 18 of 58 submissions, 31%

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Cited By

View all
  • (2024)ID-SR: Privacy-Preserving Social Recommendation Based on Infinite Divisibility for Trustworthy AIACM Transactions on Knowledge Discovery from Data10.1145/363941218:7(1-25)Online publication date: 2-Jan-2024
  • (2024)FSPPCFs: a privacy-preserving collaborative filtering recommendation scheme based on fuzzy C-means and Shapley valueComplex & Intelligent Systems10.1007/s40747-024-01758-911:1Online publication date: 30-Dec-2024
  • (2023)Personalized Privacy Protection-Preserving Collaborative Filtering Algorithm for Recommendation SystemsApplied Sciences10.3390/app1307460013:7(4600)Online publication date: 5-Apr-2023
  • (2023)More Efficient Secure Matrix Multiplication for Unbalanced Recommender SystemsIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2021.313931820:1(551-562)Online publication date: 1-Jan-2023
  • (2023)Research on Matrix Factorization Recommendation Algorithm Based on Local Differential PrivacyMethods and Applications for Modeling and Simulation of Complex Systems10.1007/978-981-99-7240-1_18(230-241)Online publication date: 13-Oct-2023
  • (2022)TEE-based decentralized recommender systems: The raw data sharing redemption2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS53621.2022.00050(447-458)Online publication date: May-2022
  • (2022)Confluence of Cryptography and Differential Privacy: A Hybrid Approach for Privacy Preserving Collaborative FilteringProceedings of the International Conference on Paradigms of Communication, Computing and Data Sciences10.1007/978-981-16-5747-4_29(333-345)Online publication date: 1-Jan-2022
  • (2021)PrivItem2Vec: A privacy-preserving algorithm for top-N recommendationInternational Journal of Distributed Sensor Networks10.1177/1550147721106125017:12(155014772110612)Online publication date: 1-Dec-2021
  • (2021)Application of Differential Privacy for Collaborative Filtering Based Recommendation System: A Survey2021 12th International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)10.1109/PAAP54281.2021.9720452(97-101)Online publication date: 10-Dec-2021
  • (2020)Privacy-Preserving Distributed Analytics in Fog-Enabled IoT SystemsSensors10.3390/s2021615320:21(6153)Online publication date: 29-Oct-2020
  • Show More Cited By

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