Accuracy/Efficiency Trade-Off for Privacy-Preserving Division Protocol

被引:0
|
作者
Ohata, Satsuya [1 ]
Morita, Hiraku [1 ]
Hanaoka, Goichiro [1 ]
机构
[1] AIST, Tokyo, Japan
关键词
MULTIPARTY COMPUTATION;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Secure multi-party computation (MPC) allows a set of parties to jointly compute a function, while keeping their inputs private. We can consider many applications of MPC and various operations/protocols for MPC have been proposed. We focus on (privacy-preserving) division protocols in this paper. Although division is included in four arithmetic operations, we need extremely high computation/communication costs in privacypreserving settings compared with other operations. Especially, we need many communication rounds for error correction to get accurate quotients. Since we iterate error correction procedure for several times in the division protocol, we can expect to reduce communication rounds by removing error correcting iterations. In this strategy, however, we cannot obtain accurate quotients. In this paper, we show experimental results about a relation between accuracy of quotients obtained by the round-reduced division protocol and the number of communication rounds we need. From our results, we find the error of quotients becomes less than 0:1% even if we reduce the number of communication rounds for error correction to 33%. This property will be useful when we make concrete applications efficient in some cases.
引用
收藏
页码:535 / 539
页数:5
相关论文
共 50 条
  • [1] Efficiency-Fairness Trade-off in Privacy-Preserving Autonomous Demand Side Management
    Baharlouei, Zahra
    Hashemi, Massoud
    IEEE TRANSACTIONS ON SMART GRID, 2014, 5 (02) : 799 - 808
  • [2] Personalized Privacy-Preserving Federated Learning: Optimized Trade-off Between Utility and Privacy
    Zhou, Jinhao
    Su, Zhou
    Ni, Jianbing
    Wang, Yuntao
    Pan, Yanghe
    Xing, Rui
    2022 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM 2022), 2022, : 4872 - 4877
  • [3] Privacy-Functionality Trade-Off: A Privacy-Preserving Multi-Channel Smart Metering System
    Zhang, Xiao-Yu
    Kuenzel, Stefanie
    Cordoba-Pachon, Jose-Rodrigo
    Watkins, Chris
    ENERGIES, 2020, 13 (12)
  • [4] AI in Healthcare Data Privacy-Preserving: Enhanced Trade-Off Between Security and Utility
    Peng, Lian
    Qiu, Meikang
    KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, PT III, KSEM 2024, 2024, 14886 : 349 - 360
  • [5] The Accuracy-Privacy Trade-off of Mobile Crowdsensing
    Abu Alsheikh, Mohammad
    Jiao, Yutao
    Niyato, Dusit
    Wang, Ping
    Leong, Derek
    Han, Zhu
    IEEE COMMUNICATIONS MAGAZINE, 2017, 55 (06) : 132 - 139
  • [6] PRIVACY-ACCURACY TRADE-OFF OF INFERENCE AS SERVICE
    Jin, Yulu
    Lai, Lifeng
    2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), 2021, : 2645 - 2649
  • [7] On Constant-Time-Identification and Privacy-Preserving RFID Protocols: Trade-Off Between Time and Memory
    Chang, Jen-Chun
    Wu, Hsin-Lung
    JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 2016, 32 (04) : 887 - 901
  • [8] On Constant-Time-Identification and Privacy-Preserving RFID Protocols: Trade-Off between Time and Memory
    Chang, Jen-Chun
    Wu, Hsin-Lung
    Zhang, Daqiang
    2013 IEEE 10TH INTERNATIONAL CONFERENCE ON AND 10TH INTERNATIONAL CONFERENCE ON AUTONOMIC AND TRUSTED COMPUTING (UIC/ATC) UBIQUITOUS INTELLIGENCE AND COMPUTING, 2013, : 613 - 618
  • [9] A Privacy-Preserving Comparison Protocol
    Sutradhar, Kartick
    Om, Hari
    IEEE TRANSACTIONS ON COMPUTERS, 2023, 72 (06) : 1815 - 1821
  • [10] Optimal Accuracy-Privacy Trade-Off for Secure Computations
    Ah-Fat, Patrick
    Huth, Michael
    IEEE TRANSACTIONS ON INFORMATION THEORY, 2019, 65 (05) : 3165 - 3182