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

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Adomavičius, G., & Tuzhilin, A. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.99 · Cited by 10,347 · see citing works

Item-based collaborative filtering recommendation algorithms

Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001) · Proceedings of the 10th international conference on World Wide Web · doi:10.1145/371920.372071 · Cited by 9,135 · see citing works

Amazon.com recommendations: item-to-item collaborative filtering

Linden, G., Smith, B., & York, J. (2003) · IEEE Internet Computing · doi:10.1109/mic.2003.1167344 · Cited by 5,403 · see citing works

Differential Privacy

Dwork, C. (2006) · Lecture notes in computer science · doi:10.1007/11787006_1 · Cited by 5,362 · see citing works

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 · see citing works

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 · see citing works

Item-based top- N recommendation algorithms

Deshpande, M., & Karypis, G. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963776 · Cited by 2,192 · see citing works

De-anonymizing Social Networks

Narayanan, A., & Shmatikov, V. (2009) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2009.22 · Cited by 1,324 · see citing works

Generating Accurate Rule Sets Without Global Optimization

Frank, E., & Witten, I. H. (1998) · Research Commons (University of Waikato) · doi:10.5555/645527.657305 · Cited by 1,276 · see citing works

Resolving Individuals Contributing Trace Amounts of DNA to Highly Complex Mixtures Using High-Density SNP Genotyping Microarrays

Homer, N., Szelinger, S., Redman, M., Duggan, D., Tembe, W., Muehling, J., Pearson, J. V., Stephan, D. A., Nelson, S. F., & Craig, D. W. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000167 · Cited by 1,221 · see citing works

Revealing information while preserving privacy

Dinur, I., & Nissim, K. (2003) · ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS) · doi:10.1145/773153.773173 · Cited by 1,003 · see citing works

Differentially private recommender systems

McSherry, F., & Mironov, I. (2009) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1557019.1557090 · Cited by 676 · see citing works

Differential privacy under continual observation

Dwork, C., Naor, M., Pitassi, T., & Rothblum, G. N. (2010) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/1806689.1806787 · Cited by 607 · see citing works

Collaborative filtering with privacy

Canny, J. (2005) · Proceedings - IEEE Symposium on Security and Privacy/Proceedings of the ... IEEE Symposium on Security and Privacy · doi:10.1109/secpri.2002.1004361 · Cited by 446 · see citing works

Private and Continual Release of Statistics

Chan, T.-H. H., Shi, E., & Song, D. X. (2011) · ACM Transactions on Information and System Security · doi:10.1145/2043621.2043626 · Cited by 378 · see citing works

Learning your identity and disease from research papers

Wang, R., Li, Y. F., Wang, X., Tang, H., & Zhou, X. (2009) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1653662.1653726 · Cited by 206 · see citing works

Privacy risks in recommender systems

Ramakrishnan, N., Keller, B. J., Mirza, B. J., Grama, A. Y., & Karypis, G. (2001) · IEEE Internet Computing · doi:10.1109/4236.968832 · Cited by 197 · see citing works

Personalized social recommendations

Machanavajjhala, A., Korolova, A., & Sarma, A. D. (2011) · Proceedings of the VLDB Endowment · doi:10.14778/1988776.1988780 · Cited by 174 · see citing works

Unsupervised strategies for shilling detection and robust collaborative filtering

Mehta, B., & Nejdl, W. (2008) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-008-9050-4 · Cited by 170 · see citing works

The BellKor 2008 Solution to the Netflix Prize

Bell, R. M., Koren, Y., & Volinsky, C. T. (2007) · Cited by 132 · see citing works

Privacy-Preserving Collaborative Recommender Systems

Zhan, J., Hsieh, C.-L., Wang, I.-C., Hsu, T., Liau, C., & Wang, D. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2010.2040275 · Cited by 117 · see citing works

Large Online Social Footprints--An Emerging Threat

Irani, D., Webb, S., Li, K., & Pu, C. (2009) · doi:10.1109/cse.2009.459 · Cited by 103 · see citing works

Preserving privacy in collaborative filtering through distributed aggregation of offline profiles

Shokri, R., Pedarsani, P., Theodorakopoulos, G., & Hubaux, J. (2009) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/1639714.1639741 · Cited by 97 · see citing works

Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems

Mobasher, B., Burke, R., Bhaumik, R., & Williams, C. (2005) · Cited by 95 · see citing works

Private and Continual Release of Statistics

Chan, T.-H. H., Shi, E., & Song, D. X. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-14162-1_34 · Cited by 86 · see citing works

You are what you say

Frankowski, D., Cosley, D., Sen, S., Terveen, L., & Riedl, J. (2006) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1148170.1148267 · Cited by 85 · see citing works

Privacy-Preserving Top-N Recommendation on Horizontally Partitioned Data

Polat, H., & Du, W. K. (2005) · doi:10.1109/wi.2005.117 · Cited by 50 · see citing works

Empirical Analysis of the Business Value of Recommender SystemsPreprint

Garfinkel, R., Gopal, R. D., Pathak, B. K., Venkatesan, R., & Yin, F. (2007) · SSRN Electronic Journal · doi:10.2139/ssrn.958770 · Cited by 17 · see citing works

Differentially private recommender systems

McSherry, F., & Mironov, I. (2009) · Cited by 16 · see citing works

Membership Inference Attacks Against Machine Learning Models

Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754 · see citing works

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

Yeom, S., Giacomelli, I., Fredrikson, M., & Jha, S. (2018) · 2018 IEEE 31st Computer Security Foundations Symposium (CSF) · doi:10.1109/csf.2018.00027 · Cited by 1,001 · see citing works

Differential privacy under continual observation

Dwork, C., Naor, M., Pitassi, T., & Rothblum, G. N. (2010) · ACM Symposium on Theory of Computing (STOC) · doi:10.1145/1806689.1806787 · Cited by 607 · see citing works

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 · see citing works

LOGAN: Membership Inference Attacks Against Generative Models

Hayes, J., Melis, L., Danezis, G., & De Cristofaro, E. (2019) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2019-0008 · Cited by 433 · see citing works

Private and Continual Release of Statistics

Chan, T.-H. H., Shi, E., & Song, D. X. (2011) · ACM Transactions on Information and System Security · doi:10.1145/2043621.2043626 · Cited by 378 · see citing works

Differentially Private Filtering

Ny, J. L., & Pappas, G. J. (2013) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2013.2283096 · Cited by 345 · see citing works

Algorithms that remember: model inversion attacks and data protection law

Veale, M., Binns, R., & Edwards, L. (2018) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2018.0083 · Cited by 278 · see citing works

Exposed! A Survey of Attacks on Private Data

Dwork, C., Smith, A., Steinke, T., & Ullman, J. (2017) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-060116-054123 · Cited by 259 · see citing works

Local differential privacy and its applications: A comprehensive survey

Yang, M., Guo, T., Zhu, T., Tjuawinata, I., Zhao, J., & Lam, K. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103827 · Cited by 270 · see citing works

Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy

Shin, H., Kim, S., Shin, J., & Xiao, X. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2805356 · Cited by 229 · see citing works

Poisoning Attacks to Graph-Based Recommender Systems

Fang, M., Yang, G., Gong, N. Z., & Liu, J. (2018) · Annual Computer Security Applications Conference · doi:10.1145/3274694.3274706 · Cited by 211 · see citing works

Differentially private filtering

Ny, J. L., & Pappas, G. J. (2012) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc.2012.6426355 · Cited by 188 · see citing works

Personalized social recommendations

Machanavajjhala, A., Korolova, A., & Sarma, A. D. (2011) · Proceedings of the VLDB Endowment · doi:10.14778/1988776.1988780 · Cited by 174 · see citing works

Memento: Learning Secrets from Process Footprints

Jana, S., & Shmatikov, V. (2012) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2012.19 · Cited by 167 · see citing works

Machine Unlearning: A Survey

Xu, H., Zhu, T., Zhang, L., Zhou, W., & Yu, P. S. (2024) · ACM Computing Surveys · doi:10.1145/3603620 · Cited by 164 · see citing works

Differential Privacy in Practice: Expose your Epsilons!

Dwork, C., Kohli, N., & Mulligan, D. K. (2019) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.689 · Cited by 147 · see citing works

Latest trends of security and privacy in recommender systems: A comprehensive review and future perspectives

Himeur, Y., Sohail, S. S., Bensaali, F., Amira, A., & Alazab, M. (2022) · Computers & Security · doi:10.1016/j.cose.2022.102746 · Cited by 139 · see citing works

Applying Differential Privacy to Matrix Factorization

Berlioz, A., Friedman, A., Kâafar, M. A., Boreli, R., & Berkovsky, S. (2015) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2792838.2800173 · Cited by 126 · see citing works

Privacy Aspects of Recommender Systems

Friedman, A., Knijnenburg, B. P., Vanhecke, K., Martens, L., & Berkovsky, S. (2015) · Springer eBooks · doi:10.1007/978-1-4899-7637-6_19 · Cited by 123 · see citing works

Data Poisoning Attacks to Deep Learning Based Recommender Systems

Huang, H., Mu, J., Gong, N. Z., Li, Q., Liu, B., & Xu, M. (2021) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2021.24525 · Cited by 120 · see citing works

A Survey on Trustworthy Recommender Systems

Ge, Y., Liu, S., Fu, Z., Tan, J., Li, Z., Xu, S., Li, Y., Xian, Y., & Zhang, Y. (2024) · ACM Transactions on Recommender Systems · doi:10.1145/3652891 · Cited by 144 · see citing works

A differential privacy framework for matrix factorization recommender systems

Friedman, A., Berkovsky, S., & Kâafar, M. A. (2016) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-016-9177-7 · Cited by 111 · see citing works

Privacy via the Johnson-Lindenstrauss Transform

Kenthapadi, K., Korolova, A., Mironov, I., & Mishra, N. (2013) · Journal of Privacy and Confidentiality · doi:10.29012/jpc.v5i1.625 · Cited by 111 · see citing works

Can Big Data Protect a Firm from Competition?Preprint

Lambrecht, A., & Tucker, C. E. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2705530 · Cited by 109 · see citing works

Dimensions and Metrics for Evaluating Recommendation Systems

Avazpour, I., Pitakrat, T., Grunske, L., & Grundy, J. (2013) · Springer eBooks · doi:10.1007/978-3-642-45135-5_10 · Cited by 108 · see citing works

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis

Wang, T., Zhang, X., Feng, J., & Yang, X. (2020) · Sensors · doi:10.3390/s20247030 · Cited by 106 · see citing works

The Cracked Cookie Jar: HTTP Cookie Hijacking and the Exposure of Private Information

Sivakorn, S., Polakis, I., & Keromytis, A. D. (2016) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2016.49 · Cited by 100 · see citing works

Graph Embedding for Recommendation against Attribute Inference Attacks

Zhang, S., Yin, H., Chen, T., Huang, Z. H., Cui, L., & Zhang, X. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449813 · Cited by 98 · see citing works

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 · see citing works

Attacking Recommender Systems with Augmented User Profiles

Lin, C., Chen, S., Li, H., Xiao, Y., Li, L., & Yang, Q. (2020) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3340531.3411884 · Cited by 92 · see citing works

Use Fewer Instances of the Letter “i”: Toward Writing Style Anonymization

McDonald, A., Afroz, S., Caliskan, A., Stolerman, A., & Greenstadt, R. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-31680-7_16 · Cited by 87 · see citing works

Trusted AI in Multiagent Systems: An Overview of Privacy and Security for Distributed Learning

Ma, C., Li, J., Wei, K., Liu, B., Ding, M., Yuan, L., Han, Z., & Vincent Poor, H. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3306773 · Cited by 96 · see citing works

UnSplit

Erdoğan, E., Küpçü, A., & Çiçek, A. E. (2022) · Workshop on Privacy in the Electronic Society · doi:10.1145/3559613.3563201 · Cited by 83 · see citing works

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 · see citing works

Algorithms for Differentially Private Multi-Armed Bandits

Tossou, A. C. Y., & Christos Dimitrakakis, A. (2016) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v30i1.10212 · Cited by 77 · see citing works

Privacy-Preserving Personalized Recommendation: An Instance-Based Approach via Differential Privacy

Shen, Y., & Jin, H. (2014) · Proceedings · doi:10.1109/icdm.2014.140 · Cited by 76 · see citing works

PeGaSus

Chen, Y., Machanavajjhala, A., Hay, M., & Miklau, G. (2017) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3133956.3134102 · Cited by 76 · see citing works

Peace: Privacy-Preserving and Cost-Efficient Task Offloading for Mobile-Edge Computing

He, X., Jin, R., & Dai, H. (2019) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2019.2958091 · Cited by 73 · see citing works

Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback

Minto, L., Haller, M., Livshits, B., & Haddadi, H. (2021) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3460231.3474262 · Cited by 74 · see citing works

On differentially private low rank approximation

Kapralov, M., & Talwar, K. (2013) · doi:10.5555/2627817.2627918 · Cited by 72 · see citing works

Differentially private matrix factorization

Hua, J., Xia, C., & Zhong, S. (2015) · International Conference on Artificial Intelligence · Cited by 65 · see citing works

A Survey on Personalized News Recommendation Technology

Li, M., & Wang, L. (2019) · IEEE Access · doi:10.1109/access.2019.2944927 · Cited by 63 · see citing works

Shilling Black-Box Recommender Systems by Learning to Generate Fake User Profiles

Lin, C., Chen, S., Zeng, M., Zhang, S., Gao, M., & Li, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183210 · Cited by 62 · see citing works

Differential privacy for neighborhood-based collaborative filtering

Zhu, T., Li, G., Ren, Y., Zhou, W., & Xiong, P. (2013) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2492517.2492519 · Cited by 61 · see citing works

Covering the Sensitive Subjects to Protect Personal Privacy in Personalized Recommendation

Wu, Z., Li, G., Liu, Q., Xu, G., & Chen, E. (2016) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2016.2575825 · Cited by 57 · see citing works

Privacy-preserving Data Mining in Industry

Kenthapadi, K., Mironov, I., & Thakurta, A. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291384 · Cited by 57 · see citing works

FedCT: Federated Collaborative Transfer for Recommendation

Liu, S., Xu, S., Yu, W., Fu, Z., Zhang, Y., & Marian, A. (2021) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3404835.3462825 · Cited by 55 · see citing works

An effective privacy preserving algorithm for neighborhood-based collaborative filtering

Zhu, T., Ren, Y., Zhou, W., Rong, J., & Xiong, P. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.07.019 · Cited by 55 · see citing works

AI alignment: Assessing the global impact of recommender systems

Bojić, L. (2024) · Futures · doi:10.1016/j.futures.2024.103383 · Cited by 50 · see citing works

(Nearly) optimal differentially private stochastic multi-arm bandits

Mishra, N., & Thakurta, A. (2015) · Cited by 49 · see citing works

An Efficient Blockchain-Based Privacy-Preserving Collaborative Filtering Architecture

Casino, F., & Patsakis, C. (2019) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2019.2944279 · Cited by 46 · see citing works

Differentially-Private Federated Linear BanditsPreprint

Dubey, A., & Pentland, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.11425 · Cited by 46 · see citing works

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 · see citing works

Towards a more reliable privacy-preserving recommender system

Jiang, J.-Y., Li, C., & Lin, S.-D. (2019) · Information Sciences · doi:10.1016/j.ins.2018.12.085 · Cited by 44 · see citing works

Multilingual Opinion Mining Movie Recommendation System Using RNN

Singh, T., Nayyar, A., & Solanki, A. (2020) · Lecture notes in networks and systems · doi:10.1007/978-981-15-3369-3_44 · Cited by 44 · see citing works

A differentially private nonnegative matrix factorization for recommender system

Ran, X., Wang, Y., Zhang, L. Y., & Ma, J. (2022) · Information Sciences · doi:10.1016/j.ins.2022.01.050 · Cited by 44 · see citing works

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 · see citing works

πBox: a platform for privacy-preserving apps

Lee, S., Wong, E. L., Goel, D., Dahlin, M., & Shmatikov, V. (2013) · Cited by 41 · see citing works

Privacy-preserving Cross-domain Location Recommendation

Gao, C., Huang, C., Yu, Y., Wang, H., Li, Y., & Jin, D. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3314398 · Cited by 41 · see citing works

Differentially Private MIMO Filtering for Event Streams

Ny, J. L., & Mohammady, M. (2017) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2017.2713643 · Cited by 40 · see citing works

Locally differentially private item-based collaborative filtering

Guo, T., Luo, J., Dong, K., & Yang, M. (2019) · Information Sciences · doi:10.1016/j.ins.2019.06.021 · Cited by 40 · see citing works

DPLCF

Gao, C., Huang, C., Lin, D., Jin, D., & Li, Y. (2020) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3397271.3401053 · Cited by 40 · see citing works

Overfitting, robustness, and malicious algorithms: A study of potential causes of privacy risk in machine learning

Yeom, S., Giacomelli, I., Menaged, A., Fredrikson, M., & Jha, S. (2019) · Journal of Computer Security · doi:10.3233/jcs-191362 · Cited by 39 · see citing works

Recommending with an agenda

Bhagat, S., Weinsberg, U., Ioannidis, S., & Taft, N. (2014) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2645710.2645747 · Cited by 38 · see citing works

APPLET: a privacy-preserving framework for location-aware recommender system

Ma, X., Li, H., Ma, J., Jiang, Q. G., Gao, S., Xi, N., & Lu, D. (2016) · Science China Information Sciences · doi:10.1007/s11432-015-0981-4 · Cited by 38 · see citing works

Privacy-Preserving Distributed Stream Monitoring

Friedman, A., Sharfman, I., Keren, D., & Schuster, A. (2014) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2014.23128 · Cited by 37 · see citing works

Privacy accounting and quality control in the sage differentially private ML platform

Lécuyer, M., Spahn, R., Vodrahalli, K., Geambasu, R., & Hsu, D. (2019) · ACM SIGOPS Symposium on Operating Systems Principles (SOSP) · doi:10.1145/3341301.3359639 · Cited by 37 · see citing works

Data Science Data Governance [AI Ethics]

Kroll, J. A. (2018) · IEEE Security & Privacy · doi:10.1109/msec.2018.2875329 · Cited by 36 · see citing works

A privacy-preserving multimedia recommendation in the context of social network based on weighted noise injection

Dou, K., Guo, B., & Kuang, L. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4352-3 · Cited by 36 · see citing works

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 · see citing works

New SVD-based collaborative filtering algorithms with differential privacy

Xian, Z., Li, Q., Huang, X., & Li, L. (2017) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-162053 · Cited by 35 · see citing works

Enforcing Differential Privacy for Shared Collaborative Filtering

Li, J., Yang, J., Zhao, Y. D., Liu, B., Zhou, M., Bi, J., & Wang, Q. (2016) · IEEE Access · doi:10.1109/access.2016.2600258 · Cited by 35 · see citing works

Data poisoning attacks on neighborhood‐based recommender systems

Chen, L., Xu, Y., Xie, F., Huang, M., & Zheng, Z. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3872 · Cited by 34 · see citing works

EnCore

Aditya, P., Erdélyi, V., Lentz, M., Shi, E., Bhattacharjee, B., & Druschel, P. (2014) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2594368.2594374 · Cited by 34 · see citing works

A survey: deriving private information from perturbed data

Okkalıoğlu, B. D., Okkalıoğlu, M., Koç, M., & Polat, H. (2015) · Artificial Intelligence Review · doi:10.1007/s10462-015-9439-5 · Cited by 34 · see citing works

Privacy-Preserving Friendship-Based Recommender Systems

Tang, Q., & Wang, J. (2016) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2016.2631533 · Cited by 33 · see citing works

Efficient Repair of Polluted Machine Learning Systems via Causal Unlearning

Cao, Y., Yu, A. F., Aday, A., Stahl, E., Merwine, J., & Yang, J. (2018) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3196494.3196517 · Cited by 33 · see citing works

Private context-aware recommendation of points of interest: An initial investigation

Riboni, D., & Bettini, C. (2012) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2012.6197582 · Cited by 33 · see citing works

Encrypted Databases for Differential Privacy

Agarwal, A., Herlihy, M. P., Kamara, S., & Moataz, T. (2019) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2019-0042 · Cited by 32 · see citing works

Differential privacy in collaborative filtering recommender systems: a review

Müllner, P., Lex, E., Schedl, M., & Kowald, D. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2023.1249997 · Cited by 32 · see citing works

Privacy-preserving topic model for tagging recommender systems

Zhu, T., Li, G., Zhou, W., Xiong, P., & Cao, Y. Y. (2015) · Knowledge and Information Systems · doi:10.1007/s10115-015-0832-9 · Cited by 31 · see citing works

Differential Privacy for Collaborative Filtering Recommender Algorithm

Zhu, X., & Sun, Y. (2016) · ACM International Workshop on Security and Privacy Analytics (IWSPA) · doi:10.1145/2875475.2875483 · Cited by 31 · see citing works

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 · see citing works

Secure multi-party differential privacy

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Cross-domain Recommendation with Bridge-Item Embeddings

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SoK: Managing risks of linkage attacks on data privacy

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An Attack-Based Evaluation Method for Differentially Private Learning Against Model Inversion Attack

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Design and Implementation of Movie Recommendation System Based on Knn Collaborative Filtering Algorithm

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Global and Local Differential Privacy for Collaborative Bandits

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Matching user accounts across online social networks : methods and applications

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RIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning

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Differentially private Kalman filtering

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Robustness analysis of privacy-preserving model-based recommendation schemes

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Machine Learning Model for Movie Recommendation System

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Label‐only membership inference attacks on machine unlearning without dependence of posteriors

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On the Role of Data Anonymization in Machine Learning Privacy

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From existing trends to future trends in privacy‐preserving collaborative filtering

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A Survey of Privacy Solutions using Blockchain for Recommender Systems: Current Status, Classification and Open Issues

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2011 IEEE Symposium on Security and Privacy “You Might Also Like:” Privacy Risks of Collaborative Filtering

Cal, J. A., Kilzer, A., Narayanan, A., Felten, E. W., & Shmatikov, V. (2013)

Privacy risks in recommender systems

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Thwarting Passive Privacy Attacks in Collaborative Filtering

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A Critical Survey On Privacy Prevelling In Collaborative Filtring Recomender System: Challenges, State-Of-The-Art Methods And Future Directions

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Differential Privacy for Collaborative Filtering Recommender Algorithm

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