Utility-Privacy Trade-Offs with Limited Leakage for Encoder

Area of Science:

• Information Theory
• Data Privacy
• Database Security

Background:

• The increasing use of databases, including the Internet of Things (IoT), highlights the critical need for robust data privacy measures.
• Pioneering work by Yamamoto in 1983 established theoretical limits on coding rate, utility, and decoder privacy for specific database scenarios.
• Recent advancements by Shinohara and Yagi in 2022 provide a foundation for more generalized analyses.

Purpose of the Study:

• To extend the analysis of utility-privacy trade-offs in databases to a more general case.
• To introduce and analyze a measure of privacy for the encoder.
• To establish a strong converse theorem for utility-privacy trade-offs.

Main Methods:

• First-order rate analysis considering coding rate, utility (expected distortion or excess-distortion probability), decoder privacy, and encoder privacy.
• Development and application of a novel privacy measure for the encoder.
• Theoretical framework for establishing strong converse theorems in the context of utility-privacy trade-offs.

Main Results:

• Derived first-order rate-utility-privacy trade-off bounds for a generalized database model.
• Introduced a new metric for quantifying encoder privacy.
• Established a strong converse theorem for utility-privacy trade-offs when utility is measured by excess-distortion probability.

Conclusions:

• The findings offer a more comprehensive understanding of the fundamental limits governing data privacy and utility in database systems.
• The established theoretical framework may pave the way for more advanced analyses, such as second-order rate analysis.
• This research contributes to the ongoing efforts to secure sensitive information in the era of big data and IoT.