Research Article
Securek-Anonymization Linked with Differential Identifiability (Workshop)
@INPROCEEDINGS{10.1007/978-3-030-41117-6_25, author={Zheng Zhao and Tao Shang and Jianwei Liu}, title={Securek-Anonymization Linked with Differential Identifiability (Workshop)}, proceedings={Communications and Networking. 14th EAI International Conference, ChinaCom 2019, Shanghai, China, November 29 -- December 1, 2019, Proceedings, Part II}, proceedings_a={CHINACOM PART 2}, year={2020}, month={2}, keywords={Differential identifiability k-anonymization Privacy preservation}, doi={10.1007/978-3-030-41117-6_25} }- Zheng Zhao
Tao Shang
Jianwei Liu
Year: 2020
Securek-Anonymization Linked with Differential Identifiability (Workshop)
CHINACOM PART 2
Springer
DOI: 10.1007/978-3-030-41117-6_25
Abstract
Mostk-anonymization mechanisms that have been developed presently are vulnerable to re-identification attacks, e.g., those generating a generalized value based on input databases.k-anonymization mechanisms do not properly capture the notion of hiding in a crowd, because they do not impose any constraints on the mechanisms. In this paper, we define((k,\rho ))-anonymization that achieves securek-anonymization notion linked with differential identifiability under the condition of privacy parameter(\rho ). Both differential identifiability andk-anonymization limit the probability that an individual is re-identified in a database after an adversary observes the output results of the database. Furthermore, differential identifiability can provide the same strong privacy guarantees as differential privacy. It can makek-anonymization perform securely, while((k,\rho ))-anonymization achieves the relaxation of the notion of differential identifiability, which can avoid a lot of noise and help obtain better utility for certain tasks. We also prove the properties((k,\rho ))-anonymization under composition that can be used for application in data publishing and data mining.
