Towards Benchmarking Privacy Risk for Differential Privacy: A Survey

被引:0
|
作者
Prokhorenkov, Dmitry [1 ]
Cao, Yang [2 ]
机构
[1] Tech Univ Munich, Garching, Germany
[2] Hokkaido Univ, Sapporo, Hokkaido, Japan
关键词
data privacy; GDPR; differential privacy; privacy risk; attack; THREATS;
D O I
10.1145/3600100.3625373
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
While utilizing the Differential Privacy (DP) method, there are still many open issues regarding the method's effectiveness and reliability, including multiple difficulties in measuring the privacy level, which leads to implications in assessing risks while operating with personal data. Today, many experts and researchers emphasize the need and importance of utilizing DP for various kinds of data and tasks. However, to the best of our knowledge, the studies have yet to analyze the DP method regarding privacy risk assessment, thereby assessing the risks while utilizing DP and in the context of known types of privacy attacks. As a result, this study examines the existing privacy risks for various DP types in the context of present types of attacks. Also, the concept of privacy risk for DP will be analyzed, along with the corresponding metrics and their utility measurement. Our research method relies on a literature review; as a result, studies published from 2010 to 2023 were reviewed. Selected articles we examined based on the existing types of attacks, methods and metrics used to assess privacy risks. Based on this, we advanced the concept of privacy risk since none of the scientific studies clearly established the notation privacy risk for DP. Thus, we seek to explain the concept of privacy risk for DP in the context of existing types of attacks, thereby enabling DP utilization concerning the GDPR.
引用
收藏
页码:322 / 327
页数:6
相关论文
共 50 条
  • [1] Survey on Local Differential Privacy
    Ye Q.-Q.
    Meng X.-F.
    Zhu M.-J.
    Huo Z.
    Ruan Jian Xue Bao/Journal of Software, 2018, 29 (07): : 1981 - 2005
  • [2] Differential privacy: A survey of results
    Dwork, Cynthia
    THEORY AND APPLICATIONS OF MODELS OF COMPUTATION, PROCEEDINGS, 2008, 4978 : 1 - 19
  • [3] A survey on differential privacy and applications
    Xiong, Ping
    Zhu, Tian-Qing
    Wang, Xiao-Feng
    Jisuanji Xuebao/Chinese Journal of Computers, 2014, 37 (01): : 101 - 122
  • [4] Towards a Framework for Benchmarking Privacy-ABC Technologies
    Veseli, Fatbardh
    Vateva-Gurova, Tsvetoslava
    Krontiris, Ioannis
    Rannenberg, Kai
    Suri, Neeraj
    ICT SYSTEMS SECURITY AND PRIVACY PROTECTION, IFIP TC 11 INTERNATIONAL CONFERENCE, SEC 2014, 2014, 428 : 197 - 204
  • [5] Performance Benchmarking of Local Differential Privacy for Mobile Devices
    Gallindo, Lucas
    Leal, Bruno Filizola
    Cerqueira, Luiz Miguel
    Morais, Anderson
    Kim, Soohyung
    2022 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS AND OTHER AFFILIATED EVENTS (PERCOM WORKSHOPS), 2022,
  • [6] Towards Differential Privacy for Symbolic Systems
    Jones, Austin
    Leahy, Kevin
    Hale, Matthew
    2019 AMERICAN CONTROL CONFERENCE (ACC), 2019, : 372 - 377
  • [7] A Comprehensive Survey on Local Differential Privacy
    Xiong, Xingxing
    Liu, Shubo
    Li, Dan
    Cai, Zhaohui
    Niu, Xiaoguang
    SECURITY AND COMMUNICATION NETWORKS, 2020, 2020
  • [8] A Survey on Privacy Enhanced Role Based Data Aggregation via Differential Privacy
    Shaikh, Azharuddin
    Patil, Shruti
    2018 INTERNATIONAL CONFERENCE ON ADVANCES IN COMMUNICATION AND COMPUTING TECHNOLOGY (ICACCT), 2018, : 285 - 290
  • [9] LightDP: Towards Automating Differential Privacy Proofs
    Zhang, Danfeng
    Kifer, Daniel
    ACM SIGPLAN NOTICES, 2017, 52 (01) : 888 - 901
  • [10] Towards Decentralized Deep Learning with Differential Privacy
    Cheng, Hsin-Pai
    Yu, Patrick
    Hu, Haojing
    Zawad, Syed
    Yan, Feng
    Li, Shiyu
    Li, Hai
    Chen, Yiran
    CLOUD COMPUTING - CLOUD 2019, 2019, 11513 : 130 - 145