Abstract
Most k-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 secure k-anonymization notion linked with differential identifiability under the condition of privacy parameter \(\rho \). Both differential identifiability and k-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 make k-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.
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Acknowledgment
This project was supported by the National Key Research and Development Program of China (No. 2016YFC1000307) and the National Natural Science Foundation of China (No. 61571024, 61971021) for valuable helps.
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Zhao, Z., Shang, T., Liu, J. (2020). Secure k-Anonymization Linked with Differential Identifiability (Workshop). In: Gao, H., Feng, Z., Yu, J., Wu, J. (eds) Communications and Networking. ChinaCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-030-41117-6_25
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