Privacy Preservation for Friend-Recommendation Applications

被引:3
|
作者
Wang, Weicheng [1 ]
Wang, Shengling [1 ]
Hu, Jianhui [2 ]
机构
[1] Beijing Normal Univ, Coll Informat Technol & Sci, Beijing, Peoples R China
[2] Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
ALGORITHM;
D O I
10.1155/2018/1265352
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Friend-recommendation applications as one kind of typical social applications can satisfy the social contact needs of different users and become tools for developing a social relationship. However, the privacy leakage has turned into an insurmountable obstacle to the market success of such applications. Existing privacy protection approaches for social applications either introduce untrusted third parties or sacrifice information accuracy. As for friend-recommendation applications particularly, the multihop trust chain and anonymous message methods still have a defect that the hacker can act as a user to acquire information. In this paper, we put forward the privacy protection mechanism based on zero knowledge without any privacy leakage to the application server. In detail, the server knows nothing about the user's information, but can still provide users with accurate information on friend recommendation. We also analyze the potential attack methods and propose the corresponding solution. Our simulation results verify the effectivity and efficiency of our scheme.
引用
收藏
页数:11
相关论文
共 50 条
  • [21] Technique for preserving privacy on friend recommendation system by using Naive bayes classifier in OSN
    Kulal, Nilesh
    Dhamdhere, Vidya
    2017 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICICCS), 2017, : 315 - 319
  • [22] DVO plus LCLMF: A web service recommendation mechanism with QoS privacy preservation
    Li, Kui
    Ji, Yi-mu
    Liu, Shang-dong
    Wu, Fei
    Yao, Hai-chang
    He, Jing
    Liu, Qiang
    Liu, Yan-lan
    Shao, Si-si
    You, Shuai
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2022, 34 (18):
  • [23] Impact Factor-Based Group Recommendation Scheme with Privacy Preservation in MSNs
    He, Yuanyuan
    Zhang, Kuan
    Wang, Hanyi
    Li, Fenghua
    Niu, Ben
    Li, Hui
    2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2017,
  • [24] A Trust-Based Privacy-Preserving Friend Recommendation Scheme for Online Social Networks
    Guo, Linke
    Zhang, Chi
    Fang, Yuguang
    IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2015, 12 (04) : 413 - 427
  • [25] Differential-Privacy-Based Citizen Privacy Preservation in E-Government Applications
    Shi, Yajuan
    Piao, Chunhui
    Pan, Xiao
    2016 IEEE 13TH INTERNATIONAL CONFERENCE ON E-BUSINESS ENGINEERING (ICEBE), 2016, : 158 - 163
  • [26] Spatial-temporal data-driven service recommendation with privacy-preservation
    Qi, Lianyong
    Zhang, Xuyun
    Li, Shancang
    Wan, Shaohua
    Wen, Yiping
    Gong, Wenwen
    INFORMATION SCIENCES, 2020, 515 : 91 - 102
  • [27] Time-Aware Cross-Platform IoT Service Recommendation with Privacy Preservation
    Zhang, Can
    Wu, Junhua
    Yan, Chao
    Li, Guangshun
    SECURITY AND COMMUNICATION NETWORKS, 2021, 2021
  • [28] A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications
    Zhang, Yi
    Zhao, Yuying
    Li, Zhaoqing
    Cheng, Xueqi
    Wang, Yu
    Kotevska, Olivera
    Yu, Philip S.
    Derr, Tyler
    IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2024, 36 (12) : 7497 - 7515
  • [29] Temporal-aware and sparsity-tolerant hybrid collaborative recommendation method with privacy preservation
    Meng, Shunmei
    Li, Qianmu
    Zhang, Jing
    Lin, Wenmin
    Dou, Wanchun
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2020, 32 (02):
  • [30] Efficient Web APIs Recommendation With Privacy-Preservation for Mobile App Development in Industry 4.0
    Gong, Wenwen
    Zhang, Wei
    Bilal, Muhammad
    Chen, Yifei
    Xu, Xiaolong
    Wang, Weizheng
    IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (09) : 6379 - 6387