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Fig. 2. Outline of the general privacy-preserving record linkage process as described in Section 4.

Figure 2 Outline of the general privacy-preserving record linkage process as described in Section 4.

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Fig. 1. Outline of the general record linkage process as discussed in detail in Section 3. The second step in record linkage is indexing [19], which is aimed at reducing the number of comparisons that need to be conducted between records by removing as many record pairs as possible that are unlikely to correspond to matches [17]. Only pairs that are poten- tially matching, the so-called ‘candidate record pairs’ among which we expect to find matches, are brought together to be compared in detail in the next step, the
Fig. 1. Outline of the general record linkage process as discussed in detail in Section 3. The second step in record linkage is indexing [19], which is aimed at reducing the number of comparisons that need to be conducted between records by removing as many record pairs as possible that are unlikely to correspond to matches [17]. Only pairs that are poten- tially matching, the so-called ‘candidate record pairs’ among which we expect to find matches, are brought together to be compared in detail in the next step, the
Fig. 3. The 15 dimensions used to characterize privacy-preserving record linkage techniques. Abbreviations shown in brackets are those used in Table 1. The evaluation of linkage quality in a privacy-preserving context is challenging, because in PPRL access to the actual record values is unlikely to be possible as this would reveal private or confidential information about these records. How to evaluate linkage quality using any of the measures In this section we describe a taxonomy for PPRL techniques. Our aim in developing this taxonomy is to provide a clearer picture of current approaches to PPRL, and to identify gaps in these techniques which will help us to identify directions for future research. We describe 15 dimensions of PPRL which we categorize into five main
Fig. 3. The 15 dimensions used to characterize privacy-preserving record linkage techniques. Abbreviations shown in brackets are those used in Table 1. The evaluation of linkage quality in a privacy-preserving context is challenging, because in PPRL access to the actual record values is unlikely to be possible as this would reveal private or confidential information about these records. How to evaluate linkage quality using any of the measures In this section we describe a taxonomy for PPRL techniques. Our aim in developing this taxonomy is to provide a clearer picture of current approaches to PPRL, and to identify gaps in these techniques which will help us to identify directions for future research. We describe 15 dimensions of PPRL which we categorize into five main
Characterization of the privacy-preserving record linkage techniques surveyed in Section 6.
Characterization of the privacy-preserving record linkage techniques surveyed in Section 6.
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. 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].
Kan08: A multi-party approach based on a general- ization technique (k-anonymity) for person-specific bio- medical data was introduced by Kantarcioglu et al. [108] in 2008. This approach performs efficient secure joins of encrypted databases by a third party without decrypting or inferring the contents of the joined records. It is guaranteed that each record can be linked to no less than
Kan08: A multi-party approach based on a general- ization technique (k-anonymity) for person-specific bio- medical data was introduced by Kantarcioglu et al. [108] in 2008. This approach performs efficient secure joins of encrypted databases by a third party without decrypting or inferring the contents of the joined records. It is guaranteed that each record can be linked to no less than
Fig. 6. k-Anonymized records (k=2) as used by Kantarcioglu et al. [108], Inan et al. [74], and Mohammed et al. [109].
Fig. 6. k-Anonymized records (k=2) as used by Kantarcioglu et al. [108], Inan et al. [74], and Mohammed et al. [109].
Dur10: Durham et al. [113] in 2010 adopted Schnell et al.’s Bloom filters approach [114] in their work to evaluate three different PPRL approaches. They investigated deterministic classification techniques for exact comparison, probabilistic Fig. 7. Bloom filter mapping as used by Schnell et al. [114], Karakasidis et al. [115], and Durham et al. [113,128].
Dur10: Durham et al. [113] in 2010 adopted Schnell et al.’s Bloom filters approach [114] in their work to evaluate three different PPRL approaches. They investigated deterministic classification techniques for exact comparison, probabilistic Fig. 7. Bloom filter mapping as used by Schnell et al. [114], Karakasidis et al. [115], and Durham et al. [113,128].
Fig. 8. Secure edit-distance for PPRL as proposed by Atallah et al. [129]. Rav04: In 2004, Ravikumar et al. [130] used SMC techniques for secure computation of several distance functions. In their work, they presented methods for approximate comparison of values using string distance metrics, specifically TF-IDF, SoftTF-IDF and the Euclidean distance. They use a secure stochastic dot product proto- col for secure computation of these distance metrics. The protocol is developed in the setting of two parties with a HBC adversary model. The use of SMC computations for achieving privacy makes the protocol computationally intensive. To overcome this drawback, they use sampling techniques to control the amount of communication between the two parties. Experiments on the publicly available Cora bibliographic dataset [13] showed high linkage quality with average precision of 0.85 after 1000 samples.
Fig. 8. Secure edit-distance for PPRL as proposed by Atallah et al. [129]. Rav04: In 2004, Ravikumar et al. [130] used SMC techniques for secure computation of several distance functions. In their work, they presented methods for approximate comparison of values using string distance metrics, specifically TF-IDF, SoftTF-IDF and the Euclidean distance. They use a secure stochastic dot product proto- col for secure computation of these distance metrics. The protocol is developed in the setting of two parties with a HBC adversary model. The use of SMC computations for achieving privacy makes the protocol computationally intensive. To overcome this drawback, they use sampling techniques to control the amount of communication between the two parties. Experiments on the publicly available Cora bibliographic dataset [13] showed high linkage quality with average precision of 0.85 after 1000 samples.
Fig. 10. Blocking aware private record linkage using hash signatures (HS) as proposed by Al-Lawati et al. [72]. F is an array of floating-point numbers containing TF-IDF weights.
Fig. 10. Blocking aware private record linkage using hash signatures (HS) as proposed by Al-Lawati et al. [72]. F is an array of floating-point numbers containing TF-IDF weights.
Fig. 12. Reference value based similarity calculation as used by Pang et al. [103]. With ED being the edit distance function described in Section 3.2, the triangular inequality holds: ED(‘pete’,‘pedro’) < ED (‘pete’,‘peter’) + ED(‘pedro’, ‘peter’).
Fig. 12. Reference value based similarity calculation as used by Pang et al. [103]. With ED being the edit distance function described in Section 3.2, the triangular inequality holds: ED(‘pete’,‘pedro’) < ED (‘pete’,‘peter’) + ED(‘pedro’, ‘peter’).
Fig. 11. Value generalization hierarchies as used by Inan et al. [74] and Mohammed et al. [109].
Fig. 11. Value generalization hierarchies as used by Inan et al. [74] and Mohammed et al. [109].
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