Differentially private recommender systems: Building privacy into the Netflix Prize contenders

k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY

SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571

Calibrating Noise to Sensitivity in Private Data Analysis

Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272

Differential Privacy: A Survey of Results

Dwork, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-79228-4_1 · Cited by 3,542

Robust De-anonymization of Large Sparse Datasets

Narayanan, A., & Shmatikov, V. (2008) · 2008 IEEE Symposium on Security and Privacy (sp 2008) · doi:10.1109/sp.2008.33 · Cited by 2,372

Our Data, Ourselves: Privacy Via Distributed Noise Generation

Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., & Naor, M. (2006) · Lecture Notes in Computer Science · doi:10.1007/11761679_29 · Cited by 1,835

Deep Learning with Differential Privacy

Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978318 · Cited by 6,578

Introduction to Recommender Systems Handbook

Ricci, F., Rokach, L., & Shapira, B. (2011) · Recommender Systems Handbook · doi:10.1007/978-0-387-85820-3_1 · Cited by 2,378

Adversarial machine learning

Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194

Certified Robustness to Adversarial Examples with Differential Privacy

Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., & Jana, S. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00044 · Cited by 750

Towards Making Systems Forget with Machine Unlearning

Cao, Y., & Yang, J. (2015) · 2015 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2015.35 · Cited by 622

"You Might Also Like:" Privacy Risks of Collaborative Filtering

Calandrino, J. A., Kilzer, A., Narayanan, A., Felten, E. W., & Shmatikov, V. (2011) · 2011 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2011.40 · Cited by 311

Evaluating Differentially Private Machine Learning in Practice

Jayaraman, B., & Evans, D. (2019) · arXiv · doi:10.48550/arxiv.1902.08874 · Cited by 265

Trustworthy ai: A computational perspective

Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., & Tang, J. (2023) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3546872 · Cited by 261

Untitled work

(2012) · doi:10.1145/2365952.2365989 · Cited by 170

Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing

Du, M., Wang, K., Xia, Z., & Zhang, Y. (2020) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2018.2829886 · Cited by 111

Comprehensive Privacy Analysis on Federated Recommender System Against Attribute Inference Attacks

Zhang, S., Yuan, W., & Yin, H. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3295601 · Cited by 97

Improved collaborative filtering recommendation algorithm based on differential privacy protection

Yin, C., Shi, L., Sun, R., & Wang, J. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-019-02751-7 · Cited by 90

Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning

Beigi, G., Mosallanezhad, A., Guo, R., Alvari, H., Nou, A., & Liu, H. (2020) · Proceedings of the 13th International Conference on Web Search and Data Mining · doi:10.1145/3336191.3371832 · Cited by 80

Efficient Privacy-Preserving Matrix Factorization for Recommendation via Fully Homomorphic Encryption

Kim, J., Koo, D., Kim, Y., Yoon, H., Shin, J., & Kim, S. (2018) · ACM Transactions on Privacy and Security · doi:10.1145/3212509 · Cited by 74

Privacy Preserving User-Based Recommender System

Badsha, S., Yi, X., Khalil, I., & Bertino, E. (2017) · 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.248 · Cited by 72

Managing Your Private and Public Data: Bringing Down Inference Attacks Against Your Privacy

Salamatian, S., Zhang, A., du Pin Calmon, F., Bhamidipati, S., Fawaz, N., Kveton, B., Oliveira, P., & Taft, N. (2015) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2015.2442227 · Cited by 66

A survey of local differential privacy for securing internet of vehicles

Zhao, P., Zhang, G., Wan, S., Liu, G., & Umer, T. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-019-03104-0 · Cited by 66

Improving k-anonymity based privacy preservation for collaborative filtering

Wei, R., Tian, H., & Shen, H. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.02.017 · Cited by 44

An improved matrix factorization with local differential privacy based on piecewise mechanism for recommendation systems

Wang, Y., Gao, M., Ran, X., Ma, J., & Zhang, L. Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119457 · Cited by 42

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

Chen, Z., Wang, Y., Zhang, S., Zhong, H., & Chen, L. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114366 · Cited by 42

Private and Utility Enhanced Recommendations With Local Differential Privacy and Gaussian Mixture Model

Neera, J., Chen, X., Aslam, N., Wang, K., & Shu, Z. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3126577 · Cited by 42

Horizontal Federated Recommender System: A Survey

Wang, L., Zhou, H., Bao, Y., Yan, X., Shen, G., & Kong, X. (2024) · ACM Computing Surveys · doi:10.1145/3656165 · Cited by 37

Matrix factorization recommender based on adaptive Gaussian differential privacy for implicit feedback

Liu, H., Wang, Y., Zhang, Z., Deng, J., Chen, C., & Zhang, L. Y. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103720 · Cited by 34

Towards user-oriented privacy for recommender system data: A personalization-based approach to gender obfuscation for user profiles

Slokom, M., Hanjalic, A., & Larson, M. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102722 · Cited by 31

Global and Local Differential Privacy for Collaborative Bandits

Wang, H., Zhao, Q., Wu, Q., Chopra, S., Khaitan, A., & Wang, H. (2020) · Fourteenth ACM Conference on Recommender Systems · doi:10.1145/3383313.3412254 · Cited by 28

How Much Is Too Much? Leveraging Ads Audience Estimation to Evaluate Public Profile Uniqueness

Chen, T., Chaabane, A., Tournoux, P. U., Kaafar, M.-A., & Boreli, R. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-39077-7_12 · Cited by 21

Efficient Federated Matrix Factorization Against Inference Attacks

Chai, D., Wang, L., Chen, K., & Yang, Q. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3501812 · Cited by 20

Privacy-Preserving Collaborative Filtering Using Fully Homomorphic Encryption

Jumonji, S., Sakai, K., Sun, M.-T., & Ku, W.-S. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3115776 · Cited by 19

CryptoRec: Novel Collaborative Filtering Recommender Made Privacy-Preserving Easy

Wang, J., Jin, C., Tang, Q., Liu, Z., & Aung, K. M. M. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3065752 · Cited by 9

A low cost and un-cancelled laplace noise based differential privacy algorithm for spatial decompositions

Li, X., Wang, Y., Song, J., Liu, Y., Zhang, X., Zhou, K., & Li, C. (2020) · World Wide Web · doi:10.1007/s11280-019-00769-8 · Cited by 7