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Local Differential Privacy: Tools, Challenges, and Opportunities

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Web Information Systems Engineering (WISE 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1155))

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Abstract

Local Differential Privacy (LDP), where each user perturbs her data locally before sending to an untrusted party, is a new and promising privacy-preserving model. Endorsed by both academia and industry, LDP provides strong and rigorous privacy guarantee for data collection and analysis. As such, it has been recently deployed in many real products by several major software and Internet companies, including Google, Apple and Microsoft in their mainstream products such as Chrome, iOS, and Windows 10. Besides industry, it has also attracted a lot of research attention from academia. This tutorial first introduces the rationale of LDP model behind these deployed systems to collect and analyze usage data privately, then surveys the current research landscape in LDP, and finally identifies several open problems and research directions in this community.

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Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant No: 61572413, U1636205, 91646203, 61532010, 91846204 and 61532016), the Research Grants Council, Hong Kong SAR, China (Grant No: 15238116, 15222118 and C1008-16G).

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, China

    Qingqing Ye

  2. Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong, China

    Qingqing Ye & Haibo Hu

  3. Shenzhen Research Institute, Hong Kong Polytechnic University, Kowloon, Hong Kong, China

    Haibo Hu

Authors
  1. Qingqing Ye
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  2. Haibo Hu
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Correspondence to Haibo Hu .

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Editors and Affiliations

  1. University of Macau, Macau, China

    Leong Hou U

  2. Macquarie University, Sydney, NSW, Australia

    Jian Yang

  3. South China University of Technology, Guangzhou, China

    Yi Cai

  4. International Institute of Information Technology, Hyderabad, India

    Kamalakar Karlapalem

  5. Soochow University, Suzhou, China

    An Liu

  6. Hong Kong Baptist University, Hong Kong, China

    Xin Huang

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Ye, Q., Hu, H. (2020). Local Differential Privacy: Tools, Challenges, and Opportunities. In: U, L., Yang, J., Cai, Y., Karlapalem, K., Liu, A., Huang, X. (eds) Web Information Systems Engineering. WISE 2020. Communications in Computer and Information Science, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-15-3281-8_2

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  • DOI: https://doi.org/10.1007/978-981-15-3281-8_2

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