Secure and private sequence comparisons

Atallah, Mikhail J.; Kerschbaum, Florian; Du, Wenliang

doi:10.1145/1005140.1005147

Cited by 152 publications

(108 citation statements)

References 17 publications

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“…Since this is the core of a PPRL problem, there are suggestions for SMC protocols in PPRL. For example Atallah et al (2003) proposed a SMC protocol for the computation of edit distances, the number of edit operations such as inserting, deleting, substituting characters to transform one string into another string. Another SMC protocol for the computation of other string distances has been suggested by Ravikumar et al (2004).…”

Section: Secure Multiparty Computations For Pprlmentioning

confidence: 99%

Privacy‐preserving record linkage

Schnell

2015

Methodological Developments in Data Linkage

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Section: Secure Multiparty Computations For Pprlmentioning

confidence: 99%

Privacy‐preserving record linkage

Schnell

2015

Methodological Developments in Data Linkage

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“…-We can no longer afford to have the customer carry out quadratic work or communication: Whereas in [2] there was "balance" in that all participants had equal computational and communication power, in our case the participant to whom all of the data and answer belong is asymmetrically weaker and is limited to a linear amount of computation and communication (hence cannot directly participate or help in each step of the quadratic-complexity dynamic programming solution).…”

Section: Related Workmentioning

confidence: 95%

“…Recently, Atallah, Kerschbaum, and Du [2] developed an efficient protocol for sequence comparisons in the secure two-party computation framework in which each party has a private string; the protocol enables two parties to compute the edit distance of two sequences such that neither party learns anything about the private sequence of the other party. They [2] use dynamic programming to compare sequences, but in an additively split way -each party maintains a matrix, the summation of two matrices is the real matrix implicitly used to compute edit distance.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to [2], the matrix M in our protocol is additively shared between A 1 and A 2 : A 1 and A 2 each hold a matrix M ′ and M ′′ , respectively, the sum of which is the matrix M , i.e., M = M ′ + M ′′ ; the protocol will maintain this property as an invariant through all its steps. The main challenge in our protocol is that the comparands and outcome of each comparison, as well as the indices of the minimum elements, have to be shared (in the sense that neither party individually knows them).…”

Section: Frameworkmentioning

confidence: 99%

“…4. A 1 and A 2 use the minimum finding protocol for split data (described in [2]) on their respective vectors (u ′ , v ′ , w ′ ) and (u ′′ , v ′′ , w ′′ ). As a result, A 1 gets an x ′ and A 2 gets an x ′′ whose sum An improved version of the protocol A bottleneck in the above protocol is the split computation of S(λ i , µ j ): Running the secure table lookup protocol at each step of the dynamic program costs an expensive O(σ 2 ).…”

Section: Mimicking a Step Of The Dynamic Programmentioning

confidence: 99%

See 2 more Smart Citations

Secure outsourcing of sequence comparisons

Atallah

2005

Int J Inf Secur

Self Cite

119

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Abstract. Large-scale problems in the physical and life sciences are being revolutionized by Internet computing technologies, like grid computing, that make possible the massive cooperative sharing of computational power, bandwidth, storage, and data. A weak computational device, once connected to such a grid, is no longer limited by its slow speed, small amounts of local storage, and limited bandwidth: It can avail itself of the abundance of these resources that is available elsewhere on the network. An impediment to the use of "computational outsourcing" is that the data in question is often sensitive, e.g., of national security importance, or proprietary and containing commercial secrets, or to be kept private for legal requirements such as the HIPAA legislation, Gramm-Leach-Bliley, or similar laws. This motivates the design of techniques for computational outsourcing in a privacy-preserving manner, i.e., without revealing to the remote agents whose computational power is being used, either one's data or the outcome of the computation on the data. This paper investigates such secure outsourcing for widely applicable sequence comparison problems, and gives an efficient protocol for a customer to securely outsource sequence comparisons to two remote agents, such that the agents learn nothing about the customer's two private sequences or the result of the comparison. The local computations done by the customer are linear in the size of the sequences, and the computational cost and amount of communication done by the external agents are close to the time complexity of the best known algorithm for solving the problem on a single machine (i.e., quadratic, which is a huge computational burden for the kinds of massive data on which such comparisons are made). The sequence comparison problem considered arises in a large number of applications, including speech recognition, machine vision, and molecular sequence comparisons. In addition, essentially the same protocol can solve a larger class of problems whose standard dynamic programming solutions are similar in structure to the recurrence that subtends the sequence comparison algorithm.

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Privacy‐preserving record linkage

Verykios

Christen

2013

WIREs Data Min & Knowl

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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.The paper is structured as follows. We first provide the necessary background required to better understand the different aspects of record linkage as well as PPRL. We then give an overview of PPRL techniques, where we cover the main aspects that such techniques need to address. We also describe several Volume 3,

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Secure and private sequence comparisons

Cited by 152 publications

References 17 publications

Privacy‐preserving record linkage

Privacy‐preserving record linkage

Secure outsourcing of sequence comparisons

Privacy‐preserving record linkage

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