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Differential Privacy in the Local Setting

Published: 21 March 2018 Publication History

Abstract

Differential privacy has been increasingly accepted as the de facto standard for data privacy in the research community. While many algorithms have been developed for data publishing and analysis satisfying differential privacy, there have been few deployment of such techniques.

References

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Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. 2017. Collecting Telemetry Data Privately. In Advances in Neural Information Processing Systems. 3574--3583.
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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. ACM, 1054--1067.
[3]
Thông T Nguyên, Xiaokui Xiao, Yin Yang, Siu Cheung Hui, Hyejin Shin, and Junbum Shin. 2016. Collecting and Analyzing Data from Smart Device Users with Local Differential Privacy. arXiv preprint arXiv:1606.05053 (2016).
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Adam Smith, Abhradeep Thakurta, and Jalaj Upadhyay. 2017. Is Interaction Necessary for Distributed Private Learning?. In IEEE Symposium on Security and Privacy.
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Abhradeep Guha Thakurta, Andrew H Vyrros, Umesh S Vaishampayan, Gaurav Kapoor, Julien Freudinger, Vipul Ved Prakash, Arnaud Legendre, and Steven Duplinsky. 2017. Emoji frequency detection and deep link frequency. (July 11 2017). US Patent 9,705,908.
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Tianhao Wang, Jeremiah Blocki, Ninghui Li, and Somesh Jha. 2017. Locally Differentially Private Protocols for Frequency Estimation. In Proceedings of the 26th USENIX Security Symposium. 729--745.
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Tianhao Wang, Ninghui Li, and Somesh Jha. 2017. Locally Differentially Private Heavy Hitter Identification. arXiv preprint arXiv:1708.06674 (2017).
[8]
Tianhao Wang, Ninghui Li, and Somesh Jha. 2018. Locally Differentially Private Frequent Itemset Mining. In IEEE Symposium on Security and Privacy.

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Published In

cover image ACM Conferences
IWSPA '18: Proceedings of the Fourth ACM International Workshop on Security and Privacy Analytics
March 2018
72 pages
ISBN:9781450356343
DOI:10.1145/3180445
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 March 2018

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Author Tags

  1. differential privacy
  2. local differential privacy

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  • Keynote

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CODASPY '18
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IWSPA '18 Paper Acceptance Rate 4 of 11 submissions, 36%;
Overall Acceptance Rate 18 of 58 submissions, 31%

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