Selected work

Representative Papers

A Generalized Leakage Interpretation of Alpha-Mutual Information

Jan 14, 2026

This work proposes a unified interpretation of α-mutual information in the context of quantitative information flow and its connection to privacy leakage. By constructing an adversarial generalized decision model and employing Kolmogorov–Nagumo averages together with q-logarithms to characterize the adversary’s gain, the study establishes α-mutual information as a specific instance of generalized g-leakage for the first time. The theoretical link between α-mutual information and generalized g-leakage reveals that the parameter α precisely corresponds to the adversary’s degree of risk aversion. This insight yields a cohesive framework for interpreting privacy leakage through the lens of information-theoretic measures, thereby deepening the understanding of the relationship between information metrics and adversarial behavior.

1 citationsRead paper

Kolmogorov--Nagumo Mean Frameworks for Conditional Entropy

May 08, 2026

This work addresses the limited expressiveness of existing conditional entropy frameworks—such as (η,F)-entropy—which fail to encompass important formulations like the Augustin–Csiszár entropy. To overcome this, the paper introduces, for the first time, the Kolmogorov–Nagumo (KN) averaging system into conditional entropy modeling, proposing the (η,ψ)-KN mean and establishing its equivalence to the η-mean. Building on this, a generalized g-conditional entropy framework is developed, substantially broadening the class of representable conditional entropies. This new framework not only subsumes the classical EAVG model but also precisely captures the Augustin–Csiszár conditional entropy. By integrating g-vulnerability theory, convex analysis, and key information-theoretic concepts—namely conditional random entropy (CRE) and the data processing inequality (DPI)—the study provides sufficient conditions under which the proposed framework satisfies CRE and DPI, thereby laying a more universal theoretical foundation for conditional entropy.

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A Generalized Information Bottleneck Method: A Decision-Theoretic Perspective

Feb 20, 2026

This study addresses the challenge of preserving predictive information about a target variable while removing irrelevant redundancy in data compression. Building on statistical decision theory, the authors propose an ℋ-mutual information framework that satisfies conditional independence (CV) and average generalization (AVG) criteria. They establish, for the first time, an equivalence between the generalized information bottleneck problem and Expected Sample Information (ESI), thereby enabling a computable characterization of a representation’s predictive utility. An alternating optimization algorithm is further developed to efficiently approximate the Pareto frontier between compression and utility. This work extends the applicability of classical mutual information and offers a new paradigm for information bottleneck theory that balances theoretical rigor with practical utility.

0 citationsRead paper

Several Representations of $alpha$-Mutual Information and Interpretations as Privacy Leakage Measures

Jan 17, 2025

α-Mutual Information (α-MI) suffers from semantic ambiguity and insufficient theoretical grounding in quantifying privacy leakage. Method: We systematically reconstruct its theoretical foundation by (i) introducing a novel conditional Rényi entropy satisfying the “conditional entropy reduction” property and the data processing inequality; (ii) establishing multiple equivalent characterizations of α-MI—via inverse-channel variational representation—in terms of Rényi divergence and the new conditional Rényi entropy; and (iii) unifying these as privacy leakage measures tailored to generalized means and gain functions. Contribution/Results: First, we establish α-MI as a rigorous, semantically interpretable privacy leakage metric. Second, we derive a mathematically well-defined conditional Rényi entropy with transparent privacy semantics. Third, our framework provides finer-grained leakage assessment tools applicable to differential privacy, the information bottleneck, and related settings—enhancing both theoretical coherence and practical utility.

0 citationsRead paper

Recent publications

Latest Papers

Kolmogorov--Nagumo Mean Frameworks for Conditional Entropy

May 08, 2026

This work addresses the limited expressiveness of existing conditional entropy frameworks—such as (η,F)-entropy—which fail to encompass important formulations like the Augustin–Csiszár entropy. To overcome this, the paper introduces, for the first time, the Kolmogorov–Nagumo (KN) averaging system into conditional entropy modeling, proposing the (η,ψ)-KN mean and establishing its equivalence to the η-mean. Building on this, a generalized g-conditional entropy framework is developed, substantially broadening the class of representable conditional entropies. This new framework not only subsumes the classical EAVG model but also precisely captures the Augustin–Csiszár conditional entropy. By integrating g-vulnerability theory, convex analysis, and key information-theoretic concepts—namely conditional random entropy (CRE) and the data processing inequality (DPI)—the study provides sufficient conditions under which the proposed framework satisfies CRE and DPI, thereby laying a more universal theoretical foundation for conditional entropy.

0 citationsRead paper

A Generalized Information Bottleneck Method: A Decision-Theoretic Perspective

Feb 20, 2026

This study addresses the challenge of preserving predictive information about a target variable while removing irrelevant redundancy in data compression. Building on statistical decision theory, the authors propose an ℋ-mutual information framework that satisfies conditional independence (CV) and average generalization (AVG) criteria. They establish, for the first time, an equivalence between the generalized information bottleneck problem and Expected Sample Information (ESI), thereby enabling a computable characterization of a representation’s predictive utility. An alternating optimization algorithm is further developed to efficiently approximate the Pareto frontier between compression and utility. This work extends the applicability of classical mutual information and offers a new paradigm for information bottleneck theory that balances theoretical rigor with practical utility.

0 citationsRead paper

A Generalized Leakage Interpretation of Alpha-Mutual Information

Jan 14, 2026

This work proposes a unified interpretation of α-mutual information in the context of quantitative information flow and its connection to privacy leakage. By constructing an adversarial generalized decision model and employing Kolmogorov–Nagumo averages together with q-logarithms to characterize the adversary’s gain, the study establishes α-mutual information as a specific instance of generalized g-leakage for the first time. The theoretical link between α-mutual information and generalized g-leakage reveals that the parameter α precisely corresponds to the adversary’s degree of risk aversion. This insight yields a cohesive framework for interpreting privacy leakage through the lens of information-theoretic measures, thereby deepening the understanding of the relationship between information metrics and adversarial behavior.

1 citationsRead paper

Several Representations of $alpha$-Mutual Information and Interpretations as Privacy Leakage Measures

Jan 17, 2025

α-Mutual Information (α-MI) suffers from semantic ambiguity and insufficient theoretical grounding in quantifying privacy leakage. Method: We systematically reconstruct its theoretical foundation by (i) introducing a novel conditional Rényi entropy satisfying the “conditional entropy reduction” property and the data processing inequality; (ii) establishing multiple equivalent characterizations of α-MI—via inverse-channel variational representation—in terms of Rényi divergence and the new conditional Rényi entropy; and (iii) unifying these as privacy leakage measures tailored to generalized means and gain functions. Contribution/Results: First, we establish α-MI as a rigorous, semantically interpretable privacy leakage metric. Second, we derive a mathematically well-defined conditional Rényi entropy with transparent privacy semantics. Third, our framework provides finer-grained leakage assessment tools applicable to differential privacy, the information bottleneck, and related settings—enhancing both theoretical coherence and practical utility.

0 citationsRead paper