期刊
ENTROPY
卷 25, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/e25060921
关键词
utility-privacy trade-offs; source coding; Shannon theory; strong converse theorem
This study discusses the important issue of protecting data privacy in the utilization of databases such as IoT. Building upon previous work, it introduces a measure of privacy for the encoder and examines the first-order rate analysis problem among coding rate, utility, decoder privacy, and encoder privacy. It also aims to establish the strong converse theorem for utility-privacy trade-offs.
The utilization of databases such as IoT has progressed, and understanding how to protect the privacy of data is an important issue. As pioneering work, in 1983, Yamamoto assumed the source (database), which consists of public information and private information, and found theoretical limits (first-order rate analysis) among the coding rate, utility and privacy for the decoder in two special cases. In this paper, we consider a more general case based on the work by Shinohara and Yagi in 2022. Introducing a measure of privacy for the encoder, we investigate the following two problems: The first problem is the first-order rate analysis among the coding rate, utility, privacy for the decoder, and privacy for the encoder, in which utility is measured by the expected distortion or the excess-distortion probability. The second task is establishing the strong converse theorem for utility-privacy trade-offs, in which utility is measured by the excess-distortion probability. These results may lead to a more refined analysis such as the second-order rate analysis.
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