Set-Based Adaptive Distributed Differential Evolution for Anonymity-Driven Database Fragmentation

[thumbnail of s41019-021-00170-4.pdf]
s41019-021-00170-4.pdf - Published Version (2MB) | Preview
Available under license: Creative Commons Attribution

Ge, Yong-Feng ORCID: 0000-0002-5955-6295, Cao, Jinli ORCID: 0000-0002-0221-6361, Wang, Hua ORCID: 0000-0002-8465-0996, Chen, Zhenxiang ORCID: 0000-0002-4948-3803 and Zhang, Yanchun ORCID: 0000-0002-5094-5980 (2021) Set-Based Adaptive Distributed Differential Evolution for Anonymity-Driven Database Fragmentation. Data Science and Engineering, 6. pp. 380-391. ISSN 2364-1185


By breaking sensitive associations between attributes, database fragmentation can protect the privacy of outsourced data storage. Database fragmentation algorithms need prior knowledge of sensitive associations in the tackled database and set it as the optimization objective. Thus, the effectiveness of these algorithms is limited by prior knowledge. Inspired by the anonymity degree measurement in anonymity techniques such as k-anonymity, an anonymity-driven database fragmentation problem is defined in this paper. For this problem, a set-based adaptive distributed differential evolution (S-ADDE) algorithm is proposed. S-ADDE adopts an island model to maintain population diversity. Two set-based operators, i.e., set-based mutation and set-based crossover, are designed in which the continuous domain in the traditional differential evolution is transferred to the discrete domain in the anonymity-driven database fragmentation problem. Moreover, in the set-based mutation operator, each individual’s mutation strategy is adaptively selected according to the performance. The experimental results demonstrate that the proposed S-ADDE is significantly better than the compared approaches. The effectiveness of the proposed operators is verified.

Dimensions Badge

Altmetric Badge

Item type Article
DOI 10.1007/s41019-021-00170-4
Official URL
Subjects Current > FOR (2020) Classification > 4602 Artificial intelligence
Current > FOR (2020) Classification > 4606 Distributed computing and systems software
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords database fragmentation, database privacy, data storage, optimisation, algorithms
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login