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- Title
Differentially private multidimensional data publishing.
- Authors
Al-Hussaeni, Khalil; Fung, Benjamin C. M.; Iqbal, Farkhund; Liu, Junqiang; Hung, Patrick C. K.
- Abstract
Various organizations collect data about individuals for various reasons, such as service improvement. In order to mine the collected data for useful information, data publishing has become a common practice among those organizations and data analysts, research institutes, or simply the general public. The quality of published data significantly affects the accuracy of the data analysis and thus affects decision making at the corporate level. In this study, we explore the research area of privacy-preserving data publishing, i.e., publishing high-quality data without compromising the privacy of the individuals whose data are being published. Syntactic privacy models, such as k-anonymity, impose syntactic privacy requirements and make certain assumptions about an adversary’s background knowledge. To address this shortcoming, we adopt differential privacy, a rigorous privacy model that is independent of any adversary’s knowledge and insensitive to the underlying data. The published data should preserve individuals’ privacy, yet remain useful for analysis. To maintain data utility, we propose DiffMulti, a workload-aware and differentially private algorithm that employs multidimensional generalization. We devise an efficient implementation to the proposed algorithm and use a real-life data set for experimental analysis. We evaluate the performance of our method in terms of data utility, efficiency, and scalability. When compared to closely related existing methods, DiffMulti significantly improved data utility, in some cases, by orders of magnitude.
- Subjects
MULTIDIMENSIONAL databases; ACQUISITION of data; BUSINESS information services; FINANCIAL analysts; DECISION making in business
- Publication
Knowledge & Information Systems, 2018, Vol 56, Issue 3, p717
- ISSN
0219-1377
- Publication type
Article
- DOI
10.1007/s10115-017-1132-3