research-article

Global and Local Differential Privacy for Collaborative Bandits

Authors:

Shubham Chopra,

Abhinav Khaitan,

Hongning WangAuthors Info & Claims

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

Pages 150 - 159

https://doi.org/10.1145/3383313.3412254

Published: 22 September 2020 Publication History

Abstract

Collaborative bandit learning has become an emerging focus for personalized recommendation. It leverages user dependence for joint model estimation and recommendation. As such online learning solutions directly learn from users, e.g., result clicks, they bring in new challenges in privacy protection. Despite the existence of recent studies about privacy in contextual bandit algorithms, how to efficiently protect user privacy in a collaborative bandit learning environment remains unknown.

In this paper, we develop a general solution framework to achieve differential privacy in collaborative bandit algorithms, under the notion of global differential privacy and local differential privacy. The key idea is to inject noise in a bandit model’s sufficient statistics (either on server side to achieve global differential privacy or client side to achieve local differential privacy) and calibrate the noise scale with respect to the structure of collaboration among users. We study two popularly used collaborative bandit algorithms to illustrate the application of our solution framework. Theoretical analysis proves our derived private algorithms reduce the added regret caused by privacy-preserving mechanism compared to its linear bandits counterparts, i.e., collaboration actually helps to achieve stronger privacy with the same amount of injected noise. We also empirically evaluate the algorithms on both synthetic and real-world datasets to demonstrate the trade-off between privacy and utility.

Supplementary Material

p150-wang-appendix (p150-wang-appendix.pdf)

Appendix to "Global and Local Differential Privacy for Collaborative Bandits" by Wang et al., Fourteenth ACM Conference on Recommender Systems (RecSys '20).

Download
532.56 KB

References

[1]

Yasin Abbasi-yadkori, Dávid Pál, and Csaba Szepesvári. 2011. Improved Algorithms for Linear Stochastic Bandits. In NIPS. 2312–2320.

[2]

Jacob Abernethy, Chansoo Lee, Audra McMillan, and Ambuj Tewari. 2017. Online learning via differential privacy. arXiv preprint arXiv:1711.10019(2017).

[3]

Naman Agarwal and Karan Singh. 2017. The price of differential privacy for online learning. In ICML 2017. JMLR. org, 32–40.

[4]

Peter Auer. 2002. Using Confidence Bounds for Exploitation-Exploration Trade-offs. Journal of Machine Learning Research 3 (2002), 397–422.

Digital Library

[5]

Joseph A Calandrino, Ann Kilzer, Arvind Narayanan, Edward W Felten, and Vitaly Shmatikov. 2011. ” You might also like:” Privacy risks of collaborative filtering. In 2011 IEEE Symposium on Security and Privacy. IEEE, 231–246.

Digital Library

[6]

Nicolo Cesa-Bianchi, Claudio Gentile, and Giovanni Zappella. 2013. A gang of bandits. In Advances in Neural Information Processing Systems. 737–745.

[7]

T-H Hubert Chan, Elaine Shi, and Dawn Song. 2011. Private and continual release of statistics. ACM Transactions on Information and System Security (TISSEC) 14, 3(2011), 26.

Digital Library

[8]

Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate. 2011. Differentially private empirical risk minimization. Journal of Machine Learning Research 12, Mar (2011), 1069–1109.

Digital Library

[9]

Inderjit S Dhillon, Yuqiang Guan, and Brian Kulis. 2007. Weighted graph cuts without eigenvectors a multilevel approach. IEEE transactions on pattern analysis and machine intelligence 29, 11(2007), 1944–1957.

Digital Library

[10]

John C Duchi, Michael I Jordan, and Martin J Wainwright. 2013. Local privacy and statistical minimax rates. In 2013 IEEE 54th Annual Symposium on Foundations of Computer Science. IEEE, 429–438.

Digital Library

[11]

Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference. Springer, 265–284.

[12]

Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N Rothblum. 2010. Differential privacy under continual observation. In Proceedings of the forty-second ACM symposium on Theory of computing. ACM, 715–724.

Digital Library

[13]

Cynthia Dwork, Aaron Roth, 2014. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science 9, 3–4(2014), 211–407.

[14]

Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. 2014. Rappor: Randomized aggregatable privacy-preserving ordinal response. In SIGSAC 2014. ACM, 1054–1067.

Digital Library

[15]

Sarah Filippi, Olivier Cappe, Aurélien Garivier, and Csaba Szepesvári. 2010. Parametric bandits: The generalized linear case. In NIPS. 586–594.

[16]

Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Giovanni Zappella, and Evans Etrue. 2017. On context-dependent clustering of bandits. In ICML 2017. JMLR. org, 1253–1262.

[17]

Claudio Gentile, Shuai Li, and Giovanni Zappella. 2014. Online clustering of bandits. In International Conference on Machine Learning. 757–765.

[18]

Prateek Jain, Pravesh Kothari, and Abhradeep Thakurta. 2012. Differentially private online learning. In Conference on Learning Theory. 24–1.

[19]

Prateek Jain, Om Dipakbhai Thakkar, and Abhradeep Thakurta. 2018. Differentially Private Matrix Completion Revisited. In ICML. 2215–2224.

[20]

Jaya Kawale, Hung H Bui, Branislav Kveton, Long Tran-Thanh, and Sanjay Chawla. 2015. Efficient Thompson Sampling for Online Matrix-Factorization Recommendation. In NIPS. 1297–1305.

[21]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer8(2009), 30–37.

[22]

Aleksandra Korolova. 2010. Privacy violations using microtargeted ads: A case study. In ICDM 2010. IEEE, 474–482.

Digital Library

[23]

Lihong Li, Wei Chu, John Langford, and Robert E Schapire. 2010. A contextual-bandit approach to personalized news article recommendation. In Proceedings of 19th WWW. ACM, 661–670.

Digital Library

[24]

Shuai Li, Alexandros Karatzoglou, and Claudio Gentile. 2016. Collaborative filtering bandits. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, 539–548.

Digital Library

[25]

Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing1 (2003), 76–80.

[26]

Ziqi Liu, Yu-Xiang Wang, and Alexander Smola. 2015. Fast differentially private matrix factorization. In RecSys 2015. ACM, 171–178.

Digital Library

[27]

Frank McSherry and Ilya Mironov. 2009. Differentially private recommender systems: Building privacy into the netflix prize contenders. In Proceedings of the 15th ACM SIGKDD. ACM, 627–636.

[28]

Nikita Mishra and Abhradeep Thakurta. 2015. (Nearly) optimal differentially private stochastic multi-arm bandits. In Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence. AUAI Press, 592–601.

Digital Library

[29]

Seth Neel and Aaron Roth. 2018. Mitigating bias in adaptive data gathering via differential privacy. arXiv preprint arXiv:1806.02329(2018).

[30]

Roshan Shariff and Or Sheffet. 2018. Differentially Private Contextual Linear Bandits. In Advances in Neural Information Processing Systems. 4296–4306.

[31]

Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence 2009 (2009), 4.

[32]

Jun Tang, Aleksandra Korolova, Xiaolong Bai, Xueqiang Wang, and Xiaofeng Wang. 2017. Privacy loss in apple’s implementation of differential privacy on macos 10.12. arXiv preprint arXiv:1709.02753(2017).

[33]

Abhradeep Guha Thakurta and Adam Smith. 2013. (Nearly) optimal algorithms for private online learning in full-information and bandit settings. In Advances in Neural Information Processing Systems. 2733–2741.

[34]

Aristide Charles Yedia Tossou and Christos Dimitrakakis. 2017. Achieving privacy in the adversarial multi-armed bandit. In AAAI 2017.

[35]

Huazheng Wang, Qingyun Wu, and Hongning Wang. 2016. Learning hidden features for contextual bandits. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 1633–1642.

Digital Library

[36]

Huazheng Wang, Qingyun Wu, and Hongning Wang. 2017. Factorization bandits for interactive recommendation. In AAAI 2017.

[37]

Qingyun Wu, Huazheng Wang, Quanquan Gu, and Hongning Wang. 2016. Contextual bandits in a collaborative environment. In SIGIR 2016. ACM, 529–538.

Digital Library

[38]

Tianqing Zhu, Gang Li, Yongli Ren, Wanlei Zhou, and Ping Xiong. 2013. Differential privacy for neighborhood-based collaborative filtering. In ASONAM 2013. ACM, 752–759.

Digital Library

Cited By

Mudassar BTahir SKhan FShah SShah SAbbasi Q(2024)Privacy-Preserving Data Analytics in Internet of Medical ThingsFuture Internet10.3390/fi1611040716:11(407)Online publication date: 5-Nov-2024
https://doi.org/10.3390/fi16110407
Farooqi SRahman ASaad A(2024)Differential Privacy Based Federated Learning Techniques in IoMT: A Review2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)10.1109/IMCOM60618.2024.10418361(1-7)Online publication date: 3-Jan-2024
https://doi.org/10.1109/IMCOM60618.2024.10418361
Zhou RDong AYu JDing Q(2024)FedLRDP: Federated Learning Framework with Local Random Differential Privacy2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650657(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650657
Show More Cited By

Recommendations

Dynamic Global Sensitivity for Differentially Private Contextual Bandits
RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Bandit algorithms have become a reference solution for interactive recommendation. However, as such algorithms directly interact with users for improved recommendations, serious privacy concerns have been raised regarding its practical use. In this work, ...
An Emerging Strategy for Privacy Preserving Databases: Differential Privacy
HCI for Cybersecurity, Privacy and Trust
Abstract
Data De-identification and Differential Privacy are two possible approaches for providing data security and user privacy. Data de-identification is the process where the personal identifiable information of individuals is extracted to create ...
Privacy preservation in the internet of vehicles using local differential privacy and IOTA ledger
Abstract
With the growth in Vehicular Ad Hoc Network (VANET) technology, many vehicular devices are communicating with each other and with the edge nodes, generating a massive amount of data. One of the biggest challenges is to preserve users’ privacy as ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '20: Proceedings of the 14th ACM Conference on Recommender Systems

September 2020

796 pages

ISBN:9781450375832

DOI:10.1145/3383313

Copyright © 2020 ACM.

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].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 September 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

RecSys '20

Sponsor:

RecSys '20: Fourteenth ACM Conference on Recommender Systems

September 22 - 26, 2020

Virtual Event, Brazil

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
876
Total Downloads

Downloads (Last 12 months)54
Downloads (Last 6 weeks)5

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mudassar BTahir SKhan FShah SShah SAbbasi Q(2024)Privacy-Preserving Data Analytics in Internet of Medical ThingsFuture Internet10.3390/fi1611040716:11(407)Online publication date: 5-Nov-2024
https://doi.org/10.3390/fi16110407
Farooqi SRahman ASaad A(2024)Differential Privacy Based Federated Learning Techniques in IoMT: A Review2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM)10.1109/IMCOM60618.2024.10418361(1-7)Online publication date: 3-Jan-2024
https://doi.org/10.1109/IMCOM60618.2024.10418361
Zhou RDong AYu JDing Q(2024)FedLRDP: Federated Learning Framework with Local Random Differential Privacy2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650657(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650657
Liu JSun CWu TAiello M(2024)Blockchain-based Privacy-preserving Data Service Provisioning for Internet of Things2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00073(524-534)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00073
Wen QLeung CPazdor A(2023)Personalized Privacy-Preserving Semi-Centralized Recommendation System in a Trust-Based Agent Network2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom60117.2023.00369(2644-2651)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TrustCom60117.2023.00369
Tao YChen SLi FYu DYu JSheng H(2023)A Distributed Privacy-Preserving Learning Dynamics in General Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.324144235:9(9547-9561)Online publication date: 1-Sep-2023
https://doi.org/10.1109/TKDE.2023.3241442
Sanon STetteh JReddy RSchotten H(2023)Private and Secure Machine Learning in Wireless Mobile Communication2023 IEEE Future Networks World Forum (FNWF)10.1109/FNWF58287.2023.10520497(1-6)Online publication date: 13-Nov-2023
https://doi.org/10.1109/FNWF58287.2023.10520497
Sanon SLipps CSchotten H(2023)Preserving Privacy in the Age of Wireless Evolution: Differential Privacy in 5G, Beyond 5G, and 6G2023 International Conference on Future Communications and Networks (FCN)10.1109/FCN60432.2023.10543856(1-6)Online publication date: 17-Dec-2023
https://doi.org/10.1109/FCN60432.2023.10543856
Leung CMadill EWen Q(2023)A Privacy-Preserving Semi-Decentralized Personalized Recommendation System2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386507(1336-1343)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386507
Stach CGritti CBräcker JBehringer MMitschang B(2022)Protecting Sensitive Data in the Information Age: State of the Art and Future ProspectsFuture Internet10.3390/fi1411030214:11(302)Online publication date: 22-Oct-2022
https://doi.org/10.3390/fi14110302
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents