Privacy-preserving record linkage

被引:3
作者
Verykios, Vassilios S. [1 ]
Christen, Peter [2 ]
机构
[1] Hellen Open Univ, Sch Sci & Technol, Patras, Greece
[2] Australian Natl Univ, Res Sch Comp Sci, Coll Engn & Comp Sci, Canberra, ACT, Australia
关键词
SECURITY;
D O I
10.1002/widm.1101
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
It has been recognized that sharing data between organizations can be of great benefit, since it can help discover novel and valuable information that is not available in individual databases. However, as organizations are under pressure to better utilize their large databases through sharing, integration, and analysis, protecting the privacy of personal information in such databases is an increasingly difficult task. Record linkage is the task of identifying and matching records that correspond to the same real-world entity in several databases. This task implies a crucial infrastructure component in many modern information systems. Privacy and confidentiality concerns, however, commonly prevent the matching of databases that contain personal information across different organizations. In the past decade, efforts in the research area of privacy-preserving record linkage (PPRL) have aimed to develop techniques that facilitate the matching of records across databases such that besides the matched records no private or confidential information is being revealed to any organisztion involved in such a linkage, or to any external party. We discuss the development of key techniques that solve the three main subproblems of PPRL, namely privacy, linkage quality, and scaling PPRL to large databases. We then highlight open challenges in this research area. (C) 2013 Wiley Periodicals, Inc.
引用
收藏
页码:321 / 332
页数:12
相关论文
共 50 条
[21]   Secure-aware and privacy-preserving electronic health record searching in cloud environment [J].
Wang, Xiao ;
Zhang, Aiqing ;
Xie, Xiaojuan ;
Ye, Xinrong .
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, 2019, 32 (08)
[22]   Privacy-Preserving Bin-Packing With Differential Privacy [J].
Li, Tianyu ;
Erkin, Zekeriya ;
Lagendijk, Reginald L. .
IEEE OPEN JOURNAL OF SIGNAL PROCESSING, 2022, 3 :94-106
[23]   Privacy-preserving collaborative social networks [J].
Zhan, Justin ;
Blosser, Gary ;
Yang, Chris ;
Singh, Lisa .
INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS, 2008, 5075 :114-+
[24]   Privacy-preserving collaborative data mining [J].
Zhan, J ;
Chang, LW ;
Matwin, S .
FOUNDATIONS AND NOVEL APPROACHES IN DATA MINING, 2006, 9 :213-+
[25]   An Efficient Privacy-Preserving Comparison Protocol [J].
Saha, Tushar Kanti ;
Koshiba, Takeshi .
ADVANCES IN NETWORK-BASED INFORMATION SYSTEMS, NBIS-2017, 2018, 7 :553-565
[26]   Privacy-preserving naive Bayesian classification [J].
Zhan, Z ;
Chang, LW ;
Matwin, S .
Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, Vols 1and 2, 2004, :14-20
[27]   Privacy-Preserving Deep Learning and Inference [J].
Riazi, M. Sadegh ;
Koushanfar, Farinaz .
2018 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN (ICCAD) DIGEST OF TECHNICAL PAPERS, 2018,
[28]   Privacy-preserving programming using sython [J].
Gaiman, Michael ;
Simha, Rahul ;
Narahari, Bhagirath .
COMPUTERS & SECURITY, 2007, 26 (02) :130-136
[29]   Digital credentials with privacy-preserving delegation [J].
Knox, D. A. ;
Adams, C. .
SECURITY AND COMMUNICATION NETWORKS, 2011, 4 (08) :825-838
[30]   Privacy-Preserving Fog Computing Paradigm [J].
Abubaker, Nabil ;
Dervishi, Leonard ;
Ayday, Erman .
2017 IEEE CONFERENCE ON COMMUNICATIONS AND NETWORK SECURITY (CNS), 2017, :502-509