AI Article Synopsis
- The text discusses the evolution of databases, particularly in IoT, emphasizing the importance of data privacy protection.
- It references Yamamoto's theoretical work from 1983, which analyzed coding rate, utility, and privacy using public and private information as a basis.
- The paper builds on recent research by Shinohara and Yagi (2022) to tackle the analysis of privacy measures for encoders and decoders, focusing on utility through expected distortion and establishing strong privacy-utility trade-offs.
Article Abstract
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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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10297384 | PMC |
| http://dx.doi.org/10.3390/e25060921 | DOI Listing |
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