Privacy-preserving data linkage protocols

O'Keefe, Christine M.; Yung, Ming Chi; Gu, Lifang; Baxter, Rohan A.

doi:10.1145/1029179.1029203

Cited by 40 publications

(27 citation statements)

References 11 publications

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“…Two protocols that not only enable data matching, but also allow cohort extraction, have recently been proposed [42]. Combined, they facilitate the construction of a matched data set in such a way that no identifying information is revealed to any other party involved, and neither of the data holders learns which of their records have been extracted from their databases.…”

Section: Privacy-preserving Matchingmentioning

confidence: 99%

“…This process is similar to the cohort extraction protocol discussed above [42], however, because addresses are often dirty (i.e., contain errors and variations), approximate matching techniques are required. A variation of the q-gram based three-party protocol [43] does allow such a privacy-preserving geocoding, but as discussed before, the computational and communication overheads of this approach prohibits the geocoding of large databases.…”

Section: Privacy-preserving Geocodingmentioning

confidence: 99%

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Geocode Matching and Privacy Preservation

Christen

2009

Privacy, Security, and Trust in KDD

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Abstract. Geocoding is the process of matching addresses to geographic locations, such as latitudes and longitudes, or local census areas. In many applications, addresses are the key to geo-spatial data analysis and mining. Privacy and confidentiality are of paramount importance when data from, for example, cancer registries or crime databases is geocoded. Various approaches to privacy-preserving data matching, also called record linkage or entity resolution, have been developed in recent times. However, most of these approaches have not considered the specific privacy issues involved in geocode matching. This paper provides a brief introduction to privacy-preserving data and geocode matching, and using several real-world scenarios the issues involved in privacy and confidentiality for data and geocode matching are illustrated. The challenges of making privacy-preserving matching practical for real-world applications are highlighted, and potential directions for future research are discussed.

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Section: Privacy-preserving Matchingmentioning

confidence: 99%

Section: Privacy-preserving Geocodingmentioning

confidence: 99%

Geocode Matching and Privacy Preservation

Christen

2009

Privacy, Security, and Trust in KDD

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“…Various solutions for millionaire's or private equality test problem have been proposed [2,18,22]. In addition, other types of multiparty computations have followed such as set intersection [5,6,8,9,13,14,20,24], set element reduction [16], and set union [3,10,15,16]. However, PPMU itself has been paid comparatively less attention though electronic voting and shuffling algorithms solve the similar problem.…”

Section: Introductionmentioning

confidence: 99%

Constant-Round Privacy Preserving Multiset Union

Hong¹,

Kim²,

Kim³

et al. 2013

Bulletin of the Korean Mathematical Society

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Abstract. Privacy preserving multiset union (PPMU) protocol allows a set of parties, each with a multiset, to collaboratively compute a multiset union secretly, meaning that any information other than union is not revealed. We propose efficient PPMU protocols, using multiplicative homomorphic cryptosystem. The novelty of our protocol is to directly encrypt a polynomial by representing it by an element of an extension field. The resulting protocols consist of constant rounds and improve communication cost. We also prove the security of our protocol against malicious adversaries, in the random oracle model.

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“…These algorithms modify the initial data in order to preserve privacy while still providing usable data. However, modification to the original data needs to be performed carefully, as large modifications may lead to loss of important information, while minor changes may not be sufficient to protect privacy [7].…”

Section: Introductionmentioning

confidence: 99%