Security improvements for privacy-preserving quantum multiparty computation based on circular structure

被引:3
|
作者
Abulkasim, Hussein [1 ,2 ]
Mashatan, Atefeh [1 ]
Ghose, Shohini [3 ,4 ]
机构
[1] Ryerson Univ, Ted Rogers Sch Informat Technol Management, Toronto, ON, Canada
[2] New Valley Univ, Fac Sci, El Kharga, Egypt
[3] Wilfrid Laurier Univ, Dept Phys & Comp Sci, Waterloo, ON, Canada
[4] Univ Waterloo, Inst Quantum Comp, Waterloo, ON, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Secure multiparty computation; Privacy-preserving; Quantum cryptanalysis; Collusion attack;
D O I
10.1007/s11128-021-03357-w
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Deng et al. (J Inf Secur Appl 47:120-124, 2019) recently proposed a quantum multiparty collaborative computation protocol that claims that the private information of trustful participants is secure against the distrustful ones. They also analyzed the security of their model against a malicious user and claimed that it is secure. However, our work shows that Deng et al.'s protocol is insecure against both inside and outside attacks. We suggest a modification to prevent both inside and outside attacks from getting any useful information. Also, the proposed modified version allows all participated users to compute the final statistics instead of just one user.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Security improvements for privacy-preserving quantum multiparty computation based on circular structure
    Hussein Abulkasim
    Atefeh Mashatan
    Shohini Ghose
    Quantum Information Processing, 2022, 21
  • [2] Privacy-preserving quantum multi-party computation based on circular structure
    Deng, Zhiliang
    Zhang, Ying
    Zhang, Xiaorui
    Li, Lingling
    JOURNAL OF INFORMATION SECURITY AND APPLICATIONS, 2019, 47 : 120 - 124
  • [3] Privacy-Preserving Feature Selection with Secure Multiparty Computation
    Li, Xiling
    Dowsley, Rafael
    De Cock, Martine
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, 2021, 139
  • [4] Secure multiparty computation for privacy-preserving drug discovery
    Ma, Rong
    Li, Yi
    Li, Chenxing
    Wan, Fangping
    Hu, Hailin
    Xu, Wei
    Zeng, Jianyang
    BIOINFORMATICS, 2020, 36 (09) : 2872 - 2880
  • [5] Fast Privacy-Preserving Text Classification Based on Secure Multiparty Computation
    Resende, Amanda
    Railsback, Davis
    Dowsley, Rafael
    Nascimento, Anderson C. A.
    Aranha, Diego E.
    IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2022, 17 : 428 - 442
  • [6] Privacy-Preserving Deep Learning Based on Multiparty Secure Computation: A Survey
    Zhang, Qiao
    Xin, Chunsheng
    Wu, Hongyi
    IEEE INTERNET OF THINGS JOURNAL, 2021, 8 (13) : 10412 - 10429
  • [7] Privacy-Preserving Biometric Identification Using Secure Multiparty Computation
    Bringer, Julien
    Chabanne, Herve
    Patey, Alain
    IEEE SIGNAL PROCESSING MAGAZINE, 2013, 30 (02) : 42 - 52
  • [8] Extremely Efficient and Privacy-Preserving MAX/MIN Protocol Based on Multiparty Computation in Big Data
    Park, Jeongsu
    IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2024, 70 (01) : 3042 - 3055
  • [9] Privacy-preserving record linkage in large databases using secure multiparty computation
    Peeter Laud
    Alisa Pankova
    BMC Medical Genomics, 11
  • [10] Privacy-preserving record linkage in large databases using secure multiparty computation
    Laud, Peeter
    Pankova, Alisa
    BMC MEDICAL GENOMICS, 2018, 11