CoChat
Sign Up
Cited by 42Privacy-Preserving Technologies in DataRecommender Systems and TechniquesPrivacy, Security, and Data Protection

Differentially private user-based collaborative filtering recommendation based on k-means clustering

Open access
Chen, Zhili, Wang, Yu, Zhang, Shun, Zhong, Hong, Chen, Lin·2021·Expert Systems with Applications
DOI 10.1016/j.eswa.2020.114366
Open this paper in CoChat — free accountOpen PDF

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.eswa.2020.114366

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Research works printed but not linked: 24

  1. [1][1] B. Mobasher, R. Burke, R. Bhaumik, C. Williams, Effective attack models for shilling item-based collaborative filtering systems, in: Proceedings of the 2005 WebKDD Workshop, held in conjuction with ACM SIGKDD, Vol. 2005, 2005. -
  2. [2][2] C. Dwork, F. McSherry, K. Nissim, A. Smith, Calibrating noise to sensitivity in private data analysis, in: Theory of cryptography conference, Springer, 2006, pp. 265-284. doi:10.1007/11681878\_14 . - · DOI · Paper page
  3. [3][3] C. Dwork, Differential privacy: A survey of results, in: International Conference on Theory and Applications of Models of Computation, Springer, 2008, pp. 1-19. doi:10.1007/978-3-540-79228-4\_1 . - · DOI · Paper page
  4. [4][4] T. Zhu, G. Li, Y. Ren, W. Zhou, P. Xiong, Differential privacy for neighborhood-based collaborative filtering, in: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ACM, 2013, pp. 752-759. doi:10.1145/2492517. 2492519 . - · DOI
  5. [5][5] X. Zhu, Y. Sun, Differential privacy for collaborative filtering recommender algorithm, in: Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics, ACM, 2016, pp. 9-16. doi:10.1145/ 2875475.2875483 . -
  6. [6][6] N. Li, M. Lyu, D. Su, W. Yang, Differential privacy: From theory to practice, Synthesis Lectures on Information Security, Privacy, & Trust 8 (4) (2016) 1-138. doi:10.2200/S00735ED1V01Y201609SPT018 . - · DOI
  7. [7][7] G.-R. Xue, C. Lin, Q. Yang, W. Xi, H.-J. Zeng, Y. Yu, Z. Chen, Scalable collaborative filtering using cluster-based smoothing, in: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, 2005, pp. 114-121. doi:10. 1145/1076034.1076056 . -
  8. [8][8] J. Canny, Collaborative filtering with privacy, in: Security and Privacy, 2002. Proceedings. 2002 IEEE Symposium on, IEEE, 2002, pp. 45-57. doi: 10.1109/SECPRI.2002.1004361 . - · DOI
  9. [9][9] F. Armknecht, T. Strufe, An efficient distributed privacy-preserving recommendation system, in: Ad Hoc Networking Workshop (Med-Hoc-Net), 2011 The 10th IFIP Annual Mediterranean, IEEE, 2011, pp. 65-70. doi: 10.1109/Med-Hoc-Net.2011.5970495 . - · DOI
  10. [10][10] J. Brickell, V. Shmatikov, The cost of privacy: destruction of data-mining utility in anonymized data publishing, in: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2008, pp. 70-78. doi:10.1145/1401890.1401904 . - · DOI
  11. [11][11] C.-C. Chang, B. Thompson, H. W. Wang, D. Yao, Towards publishing recommendation data with predictive anonymization, in: Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security, ACM, 2010, pp. 24-35. doi:10.1109/Med-Hoc-Net.2011.5970495 . - · DOI
  12. [12][12] F. McSherry, 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, ACM, 2009, pp. 627-636. doi:10.1145/1557019.1557090 . - · DOI
  13. [13][13] J. Hua, C. Xia, S. Zhong, Differentially private matrix factorization, in: IJCAI, 2015, pp. 1763-1770. doi:10.1145/2792838.2800191 . - · DOI
  14. [14][14] X. Meng, S. Wang, K. Shu, J. Li, B. Chen, H. Liu, Y. Zhang, Personalized privacy-preserving social recommendation, in: Proc. AAAI Conf. Artif. Intell., 2018. -
  15. [15][15] T. Feng, Y. Guo, Y. Chen, A differential private collaborative filtering framework based on privacy-relevance of topics, in: Computers and Communication (ISCC), 2016 IEEE Symposium on, IEEE, 2016, pp. 946-951. doi:10.1109/ISCC.2016.7543858 . - · DOI
  16. [16][16] J. L. Herlocker, J. A. Konstan, L. G. Terveen, J. T. Riedl, Evaluating collaborative filtering recommender systems, ACM Transactions on Information Systems (TOIS) 22 (1) (2004) 5-53. doi:10.1145/963770.963772 . - · DOI · Paper page
  17. [17][17] B. Sarwar, G. Karypis, J. Konstan, J. Riedl, Item-based collaborative filtering recommendation algorithms, in: Proceedings of the 10th international conference on World Wide Web, ACM, 2001, pp. 285-295. doi:10.1145/371920.372071 . - · DOI · Paper page
  18. [18][18] C. Dwork, A. Roth, et al., The algorithmic foundations of differential privacy, Foundations and Trends R © in Theoretical Computer Science 9 (3-4) (2014) 211-407. doi:10.1561/0400000042 . - · DOI · Paper page
  19. [19][19] F. McSherry, K. Talwar, Mechanism design via differential privacy, in: Foundations of Computer Science, 2007. FOCS'07. 48th Annual IEEE Symposium on, IEEE, 2007, pp. 94-103. doi:10.1109/FOCS.2007.66 . - · DOI
  20. [20][20] J. MacQueen, et al., Some methods for classification and analysis of multivariate observations, in: Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Vol. 1, Oakland, CA, USA, 1967, pp. 281-297. -
  21. [21][21] D. Arthur, B. Manthey, H. R¨ oglin, k-means has polynomial smoothed complexity, in: Foundations of Computer Science, 2009. FOCS'09. 50th Annual IEEE Symposium on, IEEE, 2009, pp. 405-414. doi:10.1109/FOCS.2009. 14 . - · DOI
  22. [22][22] P. J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, Journal of Computational and Applied Mathematics 20 (1) (1987) 53-65. doi:10.1016/0377-0427(87)90125-7 . - · DOI · Paper page
  23. [23][23] D. J. Ketchen, C. L. Shook, The application of cluster analysis in strategic management research: An analysis and critique, Strategic Management Journal 17 (6) (1996) 441-458. doi:10.1002/(SICI)1097-0266(199606) 17:6<441::AID-SMJ819>3.0.CO;2-G . - · DOI
  24. [24][24] D. Arthur, S. Vassilvitskii, k-means++: The advantages of careful seeding, in: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, Society for Industrial and Applied Mathematics, 2007, pp. 1027-1035. doi:10.1145/2492517.2492519 . · DOI

Cited by

Sorted by citations

Authors
Chen, Z., Wang, Y., Zhang, S., Zhong, H., & Chen, L.
Affiliations
  • Anhui University (CN)
  • Sun Yat-sen University (CN)
Corresponding author
Chen, Zhili
ORCID
Venue
Expert Systems with Applications
Publisher
Elsevier BV
Published
2021-04
Volume
168
Pages
114366
ISSN
0957-4174 (ISSN-L)
DOI
10.1016/j.eswa.2020.114366
OpenAlex
W3108669106
Cited by
42
References
0
Citations by year
2026: 8 · 2025: 3 · 2024: 7 · 2023: 10 · 2022: 10 · 2021: 4
FWCI
2.36
Type
journal article
Language
en
Open access
Licence
Locations
  • Expert Systems with Applications (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (11301002, 61572031)
  • Natural Science Foundation of Anhui Province (KJ2018A0017, 2008085MF187)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
Keywords
differential privacy, user-based collaborative filtering, recommender systems, K-means clustering, exponential mechanism, privacy preservation, privacy leakage, recommendation performance

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review