Full Citation

Title: Optimization-based k-anonymity algorithms

Citation Type: Journal Article

Publication Year: 2020

ISBN:

ISSN: 01674048

DOI: 10.1016/j.cose.2020.101753

NSFID:

PMCID:

PMID:

Abstract: In this paper we present a formulation of k-anonymity as a mathematical optimization problem. In solving this formulated problem, k-anonymity is achieved while maximizing the utility of the resulting dataset. Our formulation has the advantage of incorporating different weights for attributes in order to achieve customized utility to suit different research purposes. The resulting formulation is a Mixed Integer Linear Program (MILP), which is NP-complete in general. Recognizing the complexity of the problem, we propose two practical algorithms which can provide near-optimal utility. Our experimental evaluation confirms that our algorithms are scalable when used for datasets containing large numbers of records.

Url: https://linkinghub.elsevier.com/retrieve/pii/S0167404820300377

User Submitted?: No

Authors: Liang, Yuting; Samavi, Reza

Periodical (Full): Computers & Security

Issue:

Volume: 93

Pages: 101753

Data Collections: IPUMS USA

Topics: Other, Population Data Science

Countries: