A taxonomy of privacy-preserving record linkage techniques
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TLDR
This paper presents an overview of techniques that allow the linking of databases between organizations while at the same time preserving the privacy of these data, and presents a taxonomy of PPRL techniques to characterize these techniques along 15 dimensions.About:
This article is published in Information Systems.The article was published on 2013-09-01 and is currently open access. It has received 241 citations till now. The article focuses on the topics: Record linkage & Data warehouse.read more
Figures
![Fig. 6. k-Anonymized records (k¼2) as used by Kantarcioglu et al. [108], Inan et al. [74], and Mohammed et al. [109].](/figures/fig-6-k-anonymized-records-k1-42-as-used-by-kantarcioglu-et-3vvb28qp.png)
Fig. 6. k-Anonymized records (k¼2) as used by Kantarcioglu et al. [108], Inan et al. [74], and Mohammed et al. [109]. ![Fig. 7. Bloom filter mapping as used by Schnell et al. [114], Karakasidis et al. [115], and Durham et al. [113,128].](/figures/fig-7-bloom-filter-mapping-as-used-by-schnell-et-al-114-wupq6nrg.png)
Fig. 7. Bloom filter mapping as used by Schnell et al. [114], Karakasidis et al. [115], and Durham et al. [113,128]. ![Fig. 4. Secure hash encoding for exact matching as used by Van Eycken et al. [126], Weber et al. [76], and Quantin et al. [86–89].](/figures/fig-4-secure-hash-encoding-for-exact-matching-as-used-by-van-261mhm7c.png)
Fig. 4. Secure hash encoding for exact matching as used by Van Eycken et al. [126], Weber et al. [76], and Quantin et al. [86–89]. ![Fig. 5. Multi-party private record matching using Bloom filters as proposed by Lai et al. [116].](/figures/fig-5-multi-party-private-record-matching-using-bloom-2los1a7m.png)
Fig. 5. Multi-party private record matching using Bloom filters as proposed by Lai et al. [116]. 
Fig. 1. Outline of the general record linkage proces ![Fig. 13. k-Anonymous phonetic encoding (k¼3) as proposed by Karakasidis et al. [101].](/figures/fig-13-k-anonymous-phonetic-encoding-k1-43-as-proposed-by-1zkl5ej5.png)
Fig. 13. k-Anonymous phonetic encoding (k¼3) as proposed by Karakasidis et al. [101].
![Fig. 6. k-Anonymized records (k¼2) as used by Kantarcioglu et al. [108], Inan et al. [74], and Mohammed et al. [109].](/figures/fig-6-k-anonymized-records-k1-42-as-used-by-kantarcioglu-et-3vvb28qp.webp)
![Fig. 7. Bloom filter mapping as used by Schnell et al. [114], Karakasidis et al. [115], and Durham et al. [113,128].](/figures/fig-7-bloom-filter-mapping-as-used-by-schnell-et-al-114-wupq6nrg.webp)
![Fig. 4. Secure hash encoding for exact matching as used by Van Eycken et al. [126], Weber et al. [76], and Quantin et al. [86–89].](/figures/fig-4-secure-hash-encoding-for-exact-matching-as-used-by-van-261mhm7c.webp)
![Fig. 5. Multi-party private record matching using Bloom filters as proposed by Lai et al. [116].](/figures/fig-5-multi-party-private-record-matching-using-bloom-2los1a7m.webp)
![Fig. 13. k-Anonymous phonetic encoding (k¼3) as proposed by Karakasidis et al. [101].](/figures/fig-13-k-anonymous-phonetic-encoding-k1-43-as-proposed-by-1zkl5ej5.webp)
Citations
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Book ChapterDOI
Privacy-Preserving Record Linkage with Block-Chains
TL;DR: In this article , the authors proposed a method for privacy-preserving record linkage of partially de-identified data using a block-chain to prevent privacy leaks even if all system databases and cryptographic protocols have been compromised by an adversary, including databases containing identifying information.
References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Journal ArticleDOI
k -anonymity: a model for protecting privacy
TL;DR: The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment and examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected.
Journal ArticleDOI
Space/time trade-offs in hash coding with allowable errors
TL;DR: Analysis of the paradigm problem demonstrates that allowing a small number of test messages to be falsely identified as members of the given set will permit a much smaller hash area to be used without increasing reject time.