research-article

Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory

Authors:

Kui RenAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 2, Issue 1

Article No.: 30, Pages 1 - 27

https://doi.org/10.1145/3639285

Published: 26 March 2024 Publication History

Abstract

Top-k frequent items detection is a fundamental task in data stream mining. Many promising solutions are proposed to improve memory efficiency while still maintaining high accuracy for detecting the Top-k items. Despite the memory efficiency concern, the users could suffer from privacy loss if participating in the task without proper protection, since their contributed local data streams may continually leak sensitive individual information. However, most existing works solely focus on addressing either the memory-efficiency problem or the privacy concerns but seldom jointly, which cannot achieve a satisfactory tradeoff between memory efficiency, privacy protection, and detection accuracy.

In this paper, we present a novel framework HG-LDP to achieve accurate Top-k item detection at bounded memory expense, while providing rigorous local differential privacy (LDP) protection. Specifically, we identify two key challenges naturally arising in the task, which reveal that directly applying existing LDP techniques will lead to an inferior "accuracy-privacy-memory efficiency" tradeoff. Therefore, we instantiate three advanced schemes under the framework by designing novel LDP randomization methods, which address the hurdles caused by the large size of the item domain and by the limited space of the memory. We conduct comprehensive experiments on both synthetic and real-world datasets to show that the proposed advanced schemes achieve a superior "accuracy-privacy-memory efficiency" tradeoff, saving 2300× memory over baseline methods when the item domain size is 41,270. Our code is anonymously open-sourced via the link.

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References

[1]

1999. Frequent Itemset Mining Dataset Repository, retail. http://fimi.uantwerpen.be/data/.

[2]

2004. Frequent Itemset Mining Dataset Repository, webdocs. http://fimi.uantwerpen.be/data/webdocs.dat.gz.

[3]

2015. Frequent Itemset Mining Dataset Repository, kosarak. http://fimi.uantwerpen.be/data/.

[4]

2023. How Many Videos Are on YouTube: Exploring the Vast Digital Landscape. https://www.techpluto.com/how-many-videos-are-on-youtube/.

[5]

Jayadev Acharya and Ziteng Sun. 2019. Communication complexity in locally private distribution estimation and heavy hitters. In International Conference on Machine Learning. PMLR, 51--60.

[6]

Jayadev Acharya, Ziteng Sun, and Huanyu Zhang. 2019. Hadamard response: Estimating distributions privately, efficiently, and with little communication. In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 1120--1129.

[7]

Noga Alon, Yossi Matias, and Mario Szegedy. 1999. The space complexity of approximating the frequency moments. Journal of Computer and system sciences 58, 1 (1999), 137--147.

Digital Library

[8]

Nikhil Bansal, Don Coppersmith, and Maxim Sviridenko. 2008. Improved approximation algorithms for broadcast scheduling. SIAM J. Comput. 38, 3 (2008), 1157--1174.

Digital Library

[9]

Ergute Bao, Yin Yang, Xiaokui Xiao, and Bolin Ding. 2021. CGM: an enhanced mechanism for streaming data collection with local differential privacy. Proceedings of the VLDB Endowment 14, 11 (2021), 2258--2270.

Digital Library

[10]

Ran Ben Basat, Gil Einziger, Isaac Keslassy, Ariel Orda, Shay Vargaftik, and Erez Waisbard. 2022. Memento: Making Sliding Windows Efficient for Heavy Hitters. IEEE/ACM Trans. Netw. 30, 4 (2022), 1440--1453. https://doi.org/10.1109/TNET.2021.3132385

Digital Library

[11]

Raef Bassily, Kobbi Nissim, Uri Stemmer, and Abhradeep Guha Thakurta. 2017. Practical Locally Private Heavy Hitters. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4--9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.). 2288--2296.

[12]

Ran Ben-Basat, Gil Einziger, Roy Friedman, and Yaron Kassner. 2016. Heavy hitters in streams and sliding windows. In 35th Annual IEEE International Conference on Computer Communications, INFOCOM 2016, San Francisco, CA, USA, April 10--14, 2016. IEEE, 1--9. https://doi.org/10.1109/INFOCOM.2016.7524364

Digital Library

[13]

Vladimir Braverman, Stephen R. Chestnut, Nikita Ivkin, and David P. Woodruff. 2016. Beating CountSketch for heavy hitters in insertion streams. In Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2016, Cambridge, MA, USA, June 18--21, 2016, Daniel Wichs and Yishay Mansour (Eds.). ACM, 740--753. https://doi.org/10.1145/2897518.2897558

Digital Library

[14]

T.-H. Hubert Chan, Elaine Shi, and Dawn Song. 2010. Private and Continual Release of Statistics. In Automata, Languages and Programming, 37th International Colloquium, ICALP 2010, Bordeaux, France, July 6--10, 2010, Proceedings, Part II (Lecture Notes in Computer Science, Vol. 6199), Samson Abramsky, Cyril Gavoille, Claude Kirchner, Friedhelm Meyer auf der Heide, and Paul G. Spirakis (Eds.). Springer, 405--417. https://doi.org/10.1007/978--3--642--14162--1_34

[15]

Varun Chandola, Arindam Banerjee, and Vipin Kumar. 2009. Anomaly detection: A survey. ACM Comput. Surv. 41, 3 (2009), 15:1--15:58. https://doi.org/10.1145/1541880.1541882

Digital Library

[16]

Lisi Chen and Gao Cong. 2015. Diversity-Aware Top-k Publish/Subscribe for Text Stream. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31 - June 4, 2015, Timos K. Sellis, Susan B. Davidson, and Zachary G. Ives (Eds.). ACM, 347--362. https://doi.org/10.1145/2723372.2749451

Digital Library

[17]

Yan Chen, Ashwin Machanavajjhala, Michael Hay, and Gerome Miklau. 2017. PeGaSus: Data-Adaptive Differentially Private Stream Processing. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, Dallas, TX, USA, October 30 - November 03, 2017, Bhavani Thuraisingham, David Evans, Tal Malkin, and Dongyan Xu (Eds.). ACM, 1375--1388. https://doi.org/10.1145/3133956.3134102

Digital Library

[18]

Yun Chi, Haixun Wang, Philip S Yu, and Richard R Muntz. 2004. Moment: Maintaining closed frequent itemsets over a stream sliding window. In Fourth IEEE International Conference on Data Mining (ICDM'04). IEEE, 59--66.

[19]

Graham Cormode. 2011. Sketch techniques for approximate query processing. Foundations and Trends in Databases. NOW publishers (2011), 15.

[20]

Graham Cormode and Marios Hadjieleftheriou. 2008. Finding frequent items in data streams. Proc. VLDB Endow. 1, 2 (2008), 1530--1541. https://doi.org/10.14778/1454159.1454225

Digital Library

[21]

Graham Cormode, Flip Korn, S. Muthukrishnan, and Divesh Srivastava. 2003. Finding Hierarchical Heavy Hitters in Data Streams. In Proceedings of 29th International Conference on Very Large Data Bases, VLDB 2003, Berlin, Germany, September 9--12, 2003, Johann Christoph Freytag, Peter C. Lockemann, Serge Abiteboul, Michael J. Carey, Patricia G. Selinger, and Andreas Heuer (Eds.). Morgan Kaufmann, 464--475. https://doi.org/10.1016/B978-012722442--8/50048--3

[22]

Graham Cormode, Samuel Maddock, and Carsten Maple. 2021. Frequency Estimation under Local Differential Privacy. Proc. VLDB Endow. 14, 11 (2021), 2046--2058. https://doi.org/10.14778/3476249.3476261

Digital Library

[23]

Graham Cormode and Shan Muthukrishnan. 2005. An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms 55, 1 (2005), 58--75.

Digital Library

[24]

Graham Cormode and S. Muthukrishnan. 2005. What's hot and what's not: tracking most frequent items dynamically. ACM Trans. Database Syst. 30, 1 (2005), 249--278. https://doi.org/10.1145/1061318.1061325

Digital Library

[25]

Gautam Das, Dimitrios Gunopulos, Nick Koudas, and Nikos Sarkas. 2007. Ad-hoc Top-k Query Answering for Data Streams. In Proceedings of the 33rd International Conference on Very Large Data Bases, University of Vienna, Austria, September 23--27, 2007, Christoph Koch, Johannes Gehrke, Minos N. Garofalakis, Divesh Srivastava, Karl Aberer, Anand Deshpande, Daniela Florescu, Chee Yong Chan, Venkatesh Ganti, Carl-Christian Kanne, Wolfgang Klas, and Erich J. Neuhold (Eds.). ACM, 183--194.

Digital Library

[26]

Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. 2017. Collecting telemetry data privately. Advances in Neural Information Processing Systems 30 (2017).

[27]

Cynthia Dwork. 2006. Differential Privacy. In Automata, Languages and Programming, 33rd International Colloquium, ICALP 2006, Venice, Italy, July 10--14, 2006, Proceedings, Part II (Lecture Notes in Computer Science, Vol. 4052), Michele Bugliesi, Bart Preneel, Vladimiro Sassone, and Ingo Wegener (Eds.). Springer, 1--12. https://doi.org/10.1007/11787006_1

Digital Library

[28]

Cynthia Dwork, Moni Naor, Toniann Pitassi, and Guy N. Rothblum. 2010. Differential privacy under continual observation. In Proceedings of the 42nd ACM Symposium on Theory of Computing, STOC 2010, Cambridge, Massachusetts, USA, 5--8 June 2010, Leonard J. Schulman (Ed.). ACM, 715--724. https://doi.org/10.1145/1806689.1806787

Digital Library

[29]

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. 715--724.

Digital Library

[30]

Cynthia Dwork and Aaron Roth. 2014. The Algorithmic Foundations of Differential Privacy. Found. Trends Theor. Comput. Sci. 9, 3--4 (2014), 211--407. https://doi.org/10.1561/0400000042

Digital Library

[31]

Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. 2014. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Scottsdale, AZ, USA, November 3--7, 2014, Gail-Joon Ahn, Moti Yung, and Ninghui Li (Eds.). ACM, 1054--1067. https://doi.org/10.1145/2660267.2660348

Digital Library

[32]

Úlfar Erlingsson, Vasyl Pihur, and Aleksandra Korolova. 2014. Rappor: Randomized aggregatable privacy-preserving ordinal response. In Proceedings of the 2014 ACM SIGSAC conference on computer and communications security. 1054--1067.

Digital Library

[33]

Alexandre Evfimievski, Johannes Gehrke, and Ramakrishnan Srikant. 2003. Limiting privacy breaches in privacy preserving data mining. In Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. 211--222.

Digital Library

[34]

Farhad Farokhi. 2020. Temporally discounted differential privacy for evolving datasets on an infinite horizon. In 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS). IEEE, 1--8.

[35]

Shuya Feng, Meisam Mohammady, Han Wang, Xiaochen Li, Zhan Qin, and Yuan Hong. 2023. DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming. arXiv preprint arXiv:2312.04738 (2023).

[36]

Jennifer Gillenwater, Matthew Joseph, Andres Muñoz Medina, and Mónica Ribero Diaz. 2022. A Joint Exponential Mechanism For Differentially Private Top-k. In International Conference on Machine Learning, ICML 2022, 17--23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research, Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvári, Gang Niu, and Sivan Sabato (Eds.). PMLR, 7570--7582.

[37]

Andy Greenberg. 2016. Apple's "differential privacy'is about collecting your data-but not your data. Wired, June 13 (2016).

[38]

Michael Greenwald and Sanjeev Khanna. 2001. Space-efficient online computation of quantile summaries. ACM SIGMOD Record 30, 2 (2001), 58--66.

Digital Library

[39]

Cheqing Jin, Ke Yi, Lei Chen, Jeffrey Xu Yu, and Xuemin Lin. 2010. Sliding-window top-k queries on uncertain streams. VLDB J. 19, 3 (2010), 411--435. https://doi.org/10.1007/s00778-009-0171-0

Digital Library

[40]

Matthew Joseph, Aaron Roth, Jonathan Ullman, and Bo Waggoner. 2018. Local differential privacy for evolving data. Advances in Neural Information Processing Systems 31 (2018).

[41]

Shiva Prasad Kasiviswanathan, Homin K. Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam D. Smith. 2008. What Can We Learn Privately?. In 49th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2008, October 25--28, 2008, Philadelphia, PA, USA. IEEE Computer Society, 531--540. https://doi.org/10.1109/FOCS.2008.27

Digital Library

[42]

Georgios Kellaris, Stavros Papadopoulos, Xiaokui Xiao, and Dimitris Papadias. 2014. Differentially Private Event Sequences over Infinite Streams. Proc. VLDB Endow. 7, 12 (2014), 1155--1166. https://doi.org/10.14778/2732977.2732989

Digital Library

[43]

Georg Krempl, Dariusz Brzezi'ski, Eyke Hüllermeier, Mark Last, Vincent Lemaire, Tino Noack, Ammar Shaker, Sonja Sievi, Myra Spiliopoulou, et al . 2014. Open challenges for data stream mining research. ACM SIGKDD explorations newsletter 16, 1 (2014), 1--10.

[44]

Haoyu Li, Qizhi Chen, Yixin Zhang, Tong Yang, and Bin Cui. 2022. Stingy Sketch: A Sketch Framework for Accurate and Fast Frequency Estimation. Proc. VLDB Endow. 15, 7 (2022), 1426--1438.

Digital Library

[45]

Jizhou Li, Zikun Li, Yifei Xu, Shiqi Jiang, Tong Yang, Bin Cui, Yafei Dai, and Gong Zhang. 2020. WavingSketch: An Unbiased and Generic Sketch for Finding Top-k Items in Data Streams. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23--27, 2020, Rajesh Gupta, Yan Liu, Jiliang Tang, and B. Aditya Prakash (Eds.). ACM, 1574--1584. https://doi.org/10.1145/3394486.3403208

Digital Library

[46]

Xiaochen Li, Weiran Liu, Jian Lou, Yuan Hong, Lei Zhang, Zhan Qin, and Kui Ren. 2023. Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory (full version). arXiv preprint arXiv:2311.16062 (2023).

[47]

Hongyan Liu, Xiaoyu Wang, and Yinghui Yang. 2010. Comments on "an integrated efficient solution for computing frequent and top-k elements in data streams". ACM Trans. Database Syst. 35, 2 (2010), 15:1--15:4. https://doi.org/10.1145/1735886.1735894

Digital Library

[48]

Gurmeet Singh Manku and Rajeev Motwani. 2002. Approximate Frequency Counts over Data Streams. In Proceedings of 28th International Conference on Very Large Data Bases, VLDB 2002, Hong Kong, August 20--23, 2002. Morgan Kaufmann, 346--357. https://doi.org/10.1016/B978--155860869--6/50038-X

[49]

Ahmed Metwally, Divyakant Agrawal, and Amr El Abbadi. 2005. Efficient Computation of Frequent and Top-k Elements in Data Streams. In Database Theory - ICDT 2005, 10th International Conference, Edinburgh, UK, January 5--7, 2005, Proceedings (Lecture Notes in Computer Science, Vol. 3363), Thomas Eiter and Leonid Libkin (Eds.). Springer, 398--412. https://doi.org/10.1007/978--3--540--30570--5_27

[50]

Victor Perrier, Hassan Jameel Asghar, and Dali Kaafar. 2018. Private continual release of real-valued data streams. arXiv preprint arXiv:1811.03197 (2018).

[51]

Zhan Qin, Yin Yang, Ting Yu, Issa Khalil, Xiaokui Xiao, and Kui Ren. 2016. Heavy Hitter Estimation over Set-Valued Data with Local Differential Privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, October 24--28, 2016, Edgar R. Weippl, Stefan Katzenbeisser, Christopher Kruegel, Andrew C. Myers, and Shai Halevi (Eds.). ACM, 192--203. https://doi.org/10.1145/2976749.2978409

Digital Library

[52]

Xuebin Ren, Liang Shi, Weiren Yu, Shusen Yang, Cong Zhao, and Zongben Xu. 2022. LDP-IDS: Local Differential Privacy for Infinite Data Streams. In SIGMOD '22: International Conference on Management of Data, Philadelphia, PA, USA, June 12 - 17, 2022, Zachary Ives, Angela Bonifati, and Amr El Abbadi (Eds.). ACM, 1064--1077. https://doi.org/10.1145/3514221.3526190

Digital Library

[53]

Pratanu Roy, Arijit Khan, and Gustavo Alonso. 2016. Augmented Sketch: Faster and More Accurate Stream Processing. In Proceedings of the 2016 International Conference on Management of Data, SIGMOD Conference 2016, San Francisco, CA, USA, June 26 - July 01, 2016, Fatma Özcan, Georgia Koutrika, and Sam Madden (Eds.). ACM, 1449--1463. https://doi.org/10.1145/2882903.2882948

Digital Library

[54]

Nisheeth Shrivastava, Chiranjeeb Buragohain, Divyakant Agrawal, and Subhash Suri. 2004. Medians and beyond: new aggregation techniques for sensor networks. In Proceedings of the 2nd international conference on Embedded networked sensor systems. 239--249.

Digital Library

[55]

Charles Steinfield, Thomas Adelaar, and Fang Liu. 2005. Click and Mortar Strategies Viewed from the Web: A Content Analysis of Features Illustrating Integration Between Retailers' Online and Offline Presence. Electron. Mark. 15, 3 (2005), 199--212. https://doi.org/10.1080/10196780500208632

[56]

Hao Wang and Zhengquan Xu. 2017. CTS-DP: publishing correlated time-series data via differential privacy. Knowledge-Based Systems 122 (2017), 167--179.

Digital Library

[57]

Tianhao Wang, Jeremiah Blocki, Ninghui Li, and Somesh Jha. 2017. Locally differentially private protocols for frequency estimation. In USENIX Security Symposium. 729--745.

[58]

Tianhao Wang, Joann Qiongna Chen, Zhikun Zhang, Dong Su, Yueqiang Cheng, Zhou Li, Ninghui Li, and Somesh Jha. 2021. Continuous release of data streams under both centralized and local differential privacy. In Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security. 1237--1253.

Digital Library

[59]

Tianhao Wang, Ninghui Li, and Somesh Jha. 2018. Locally Differentially Private Frequent Itemset Mining. In 2018 IEEE Symposium on Security and Privacy, SP 2018, Proceedings, 21--23 May 2018, San Francisco, California, USA. IEEE Computer Society, 127--143. https://doi.org/10.1109/SP.2018.00035

[60]

Tianhao Wang, Ninghui Li, and Somesh Jha. 2021. Locally Differentially Private Heavy Hitter Identification. IEEE Trans. Dependable Secur. Comput. 18, 2 (2021), 982--993. https://doi.org/10.1109/TDSC.2019.2927695

Digital Library

[61]

Stanley L Warner. 1965. Randomized response: A survey technique for eliminating evasive answer bias. J. Amer. Statist. Assoc. 60, 309 (1965), 63--69.

[62]

Tong Yang, Junzhi Gong, Haowei Zhang, Lei Zou, Lei Shi, and Xiaoming Li. 2018. HeavyGuardian: Separate and Guard Hot Items in Data Streams. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2018, London, UK, August 19--23, 2018, Yike Guo and Faisal Farooq (Eds.). ACM, 2584--2593. https://doi.org/10.1145/3219819.3219978

Digital Library

[63]

Ke Yi and Qin Zhang. 2009. Optimal tracking of distributed heavy hitters and quantiles. In Proceedings of the Twenty-Eigth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS 2009, June 19 - July 1, 2009, Providence, Rhode Island, USA, Jan Paredaens and Jianwen Su (Eds.). ACM, 167--174. https://doi.org/10.1145/1559795.1559820

Digital Library

[64]

Xi Zhang, Jian Cheng, Ting Yuan, Biao Niu, and Hanqing Lu. 2013. TopRec: domain-specific recommendation through community topic mining in social network. In 22nd International World Wide Web Conference, WWW '13, Rio de Janeiro, Brazil, May 13--17, 2013, Daniel Schwabe, Virgílio A. F. Almeida, Hartmut Glaser, Ricardo Baeza-Yates, and Sue B. Moon (Eds.). International World Wide Web Conferences Steering Committee / ACM, 1501--1510. https://doi.org/10.1145/2488388.2488519

Digital Library

[65]

Yang Zhou, Tong Yang, Jie Jiang, Bin Cui, Minlan Yu, Xiaoming Li, and Steve Uhlig. 2018. Cold Filter: A Meta-Framework for Faster and More Accurate Stream Processing. In Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018, Houston, TX, USA, June 10--15, 2018, Gautam Das, Christopher M. Jermaine, and Philip A. Bernstein (Eds.). ACM, 741--756. https://doi.org/10.1145/3183713.3183726

Digital Library

Cited By

Huang KOuyang GYe QHu HZheng BZhao XZhang RZhou X(2024)LDPGuard: Defenses Against Data Poisoning Attacks to Local Differential Privacy ProtocolsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.335890936:7(3195-3209)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3358909
Feng SMohammady MWang HLi XQin ZHong Y(2024)DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00124(1009-1027)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00124

Index Terms

Local Differentially Private Heavy Hitter Detection in Data Streams with Bounded Memory
1. Security and privacy
  1. Database and storage security
    1. Data anonymization and sanitization

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 2, Issue 1

SIGMOD

February 2024

1874 pages

EISSN:2836-6573

DOI:10.1145/3654807

Editor:
Divyakant Agrawal
UC Santa Barbara, United States

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Published: 26 March 2024

Published in PACMMOD Volume 2, Issue 1

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Cited By

Huang KOuyang GYe QHu HZheng BZhao XZhang RZhou X(2024)LDPGuard: Defenses Against Data Poisoning Attacks to Local Differential Privacy ProtocolsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.335890936:7(3195-3209)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3358909
Feng SMohammady MWang HLi XQin ZHong Y(2024)DPI: Ensuring Strict Differential Privacy for Infinite Data Streaming2024 IEEE Symposium on Security and Privacy (SP)10.1109/SP54263.2024.00124(1009-1027)Online publication date: 19-May-2024
https://doi.org/10.1109/SP54263.2024.00124

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