A Novel Error-Tolerant Anonymous Linking Code

Schnell, Rainer; Bachteler, Tobias; Reiher, Jörg

doi:10.2139/ssrn.3549247

Cited by 53 publications

(45 citation statements)

References 29 publications

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“…Any set-based similarity function (such as overlap, Jaccard, and Dice coefficient) can be used to calculate the similarity of pairs or sets of (multiple) BFs. The Dice coefficient has been used for matching of BFs since it is insensitive to many matching zeros (bit positions to which no q-grams are hash-mapped) in long BFs [34]. In future, we aim to investigate how other approximate string comparison functions, such as edit distance [30] and Jaro and Winkler [10,45], can be extended to calculate the similarity of more than two values.…”

Section: Dice Coefficientmentioning

confidence: 99%

“…The values for the number of hash functions used (k) and the length of the BF (l) provide a trade-off between the linkage quality and privacy [34]. The higher the value for k/l, the higher the privacy and the lower the quality of linkage, because the number of q-grams mapped to a single bit (and therefore the number of resulting collisions) increases, which leads to lower linkage quality but makes it more difficult for an adversary to learn the possible q-gram combinations [16].…”

Section: Privacy Analysismentioning

confidence: 99%

“…The higher the value for k/l, the higher the privacy and the lower the quality of linkage, because the number of q-grams mapped to a single bit (and therefore the number of resulting collisions) increases, which leads to lower linkage quality but makes it more difficult for an adversary to learn the possible q-gram combinations [16]. The CLK encoding method (as discussed in Section 2.1) of hash-mapping several QID values from each record into one compound BF [34,38] makes it even more difficult for an adversary to learn individual QID values that correspond to a revealed bit pattern in a BF.…”

Section: Privacy Analysismentioning

confidence: 99%

“…Our protocol supports approximate matching of QID values, in that data errors and variations are taken into account depending on the minimum similarity threshold st used. The quality of BF-based masking depends on the BF parameterization [34,37]. For a given BF length, l, and the number of elements g (e.g.…”

Section: Linkage Quality Analysismentioning

confidence: 99%

“…While k and l determine the computational aspects of BF masking, linkage quality and privacy will be determined by the false positive rate f . A higher value for f will mean a larger number of false matches and thus lower linkage quality [21,34]. In our experimental evaluation we will set the BF parameters for our approach according to the discussion presented here and following earlier BF work in PPRL [9,34,38,37].…”

Section: Linkage Quality Analysismentioning

confidence: 99%

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Scalable Privacy-Preserving Linking of Multiple Databases Using Counting Bloom Filters

Vatsalan

Christen

Rahm

2016

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

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Privacy-preserving record linkage (PPRL) aims at integrating sensitive information from multiple disparate databases of different organizations. PPRL approaches are increasingly required in real-world application areas such as healthcare, national security, and business. Previous approaches have mostly focused on linking only two databases as well as the use of a dedicated linkage unit. Scaling PPRL to more databases (multi-party PPRL) is an open challenge since privacy threats as well as the computation and communication costs for record linkage increase significantly with the number of databases. We thus propose the use of a new encoding method of sensitive data based on Counting Bloom Filters (CBF) to improve privacy for multi-party PPRL. We also investigate optimizations to reduce communication and computation costs for CBF-based multi-party PPRL with and without the use of a dedicated linkage unit. Empirical evaluations conducted with real datasets show the viability of the proposed approaches and demonstrate their scalability, linkage quality, and privacy protection. 4 b (n / b ) 4 b (n / b) b (n / b) b (n / b) b (n / b) Linkage Unit (LU) Candidate record sets Candidate record sets Linkage Unit (LU) b (n / b) 2 3 4 4 4 b (n / b ) b (n / b ) 4 b (n / b ) 3 b (n / b ) 2 Figure 1: An overview of traditional naïve comparison of candidate record sets masked using BFs (left) and CBFs (right, as will be described in detail in Section 3) from p = 4 parties using a LU . Databases are indexed/blocked to reduce the number of candidate record sets such that only records in the same blocks are compared and classified. Blocks are illustrated by different patterns in D i , with 1 ≤ i ≤ p. n denotes the number of records in the databases (assuming all databases are of equal size) and b denotes the number of blocks generated (assuming blocks of equal size). Independent of the masking function and the communication pattern used (i.e. BFs and direct one-to-one communication in the left figure, and CBFs and ring-based communication in the right figure), the naïve approach results in exponential complexity of b (n 4 /b 4 ) candidate sets each consisting of p = 4 records (one from each party).Contributions: Our contributions in this paper are: (1) a novel multi-party PPRL protocol based on CBFs and secure summation for efficient, approximate, and private linkage; (2) two variations of extended secure summation protocols for improved privacy against collusion among the data base owners: (a) homomorphic encryption-based and (b) salting-based (using random seed integers); (3) two efficient

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Section: Dice Coefficientmentioning

confidence: 99%

Section: Privacy Analysismentioning

confidence: 99%

Section: Privacy Analysismentioning

confidence: 99%

Section: Linkage Quality Analysismentioning

confidence: 99%

Section: Linkage Quality Analysismentioning

confidence: 99%

See 3 more Smart Citations

Scalable Privacy-Preserving Linking of Multiple Databases Using Counting Bloom Filters

Vatsalan

Christen

Rahm

2016

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)

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References

2015

Methodological Developments in Data Linkage

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Private record linkage with linkage maps

Patel

Dewri

2022

Security and Privacy

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Private record linkage is an actively pursued research area to facilitate the linkage of database records under the constraints of regulations that do not allow linkage agents to learn sensitive identities of record owners. Recent works have shown that linkage using commutative ciphers, which were discarded earlier for efficiency concerns, can be made feasible by leveraging precomputations, data parallelism, and probabilistic key reuse approaches. In this work, we propose further optimizations that can be performed to improve the runtime efficiency of such an approach. We transition from modular exponentiation ciphers to elliptic curve operations to improve precomputation time, eliminate memory intensive comparisons of encrypted values, and introduce data structures to detect negative comparisons. We benchmark the proposed approach using real world demographics data, and provide an extensive study of the parametric aspects of the approach. We also supplement our execution time results with an assessment of the residual privacy risk left by the approach. The approach can perform a linkage of two datasets with 105 records each in 20 minutes in a commodity laptop. This is achieved by eliminating the need to compare more than 70% of the record pairs. By design, the linkage accuracy is also retained at the same level as a nonprivate record linkage procedure.

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A Novel Error-Tolerant Anonymous Linking Code

Cited by 53 publications

References 29 publications

Scalable Privacy-Preserving Linking of Multiple Databases Using Counting Bloom Filters

Scalable Privacy-Preserving Linking of Multiple Databases Using Counting Bloom Filters

References

Private record linkage with linkage maps

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