Maximal Quantum Information Leakage

A new measure of information leakage for quantum encoding of classical data is defined. An adversary can access a single copy of the state of a quantum system that encodes some classical data and is interested in correctly guessing a general randomized or deterministic function of the data (e.g., a specific feature or attribute of the data in quantum machine learning) that is unknown to the security analyst. The resulting measure of information leakage, referred to as maximal quantum leakage, is the multiplicative increase of the probability of correctly guessing any function of the data upon observing measurements of the quantum state. Maximal quantum leakage is shown to satisfy post-processing inequality (i.e., applying a quantum channel reduces information leakage) and independence property (i.e., leakage is zero if the quantum state is independent of the classical data), which are fundamental properties required for privacy and security analysis. It also bounds accessible information. Effects of global and local depolarizing noise models on the maximal quantum leakage are established.

[1]  M. Wilde,et al.  Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems , 2023, ArXiv.

[2]  F. Farokhi Privacy Against Hypothesis-Testing Adversaries for Quantum Computing , 2023, ArXiv.

[3]  Daniel Stilck França,et al.  Quantum Differential Privacy: An Information Theory Perspective , 2022, IEEE Transactions on Information Theory.

[4]  Ni Ding,et al.  Measuring Information Leakage in Non-stochastic Brute-Force Guessing , 2020, 2020 IEEE Information Theory Workshop (ITW).

[5]  Michael Gastpar,et al.  Generalization Error Bounds via Rényi-, f-Divergences and Maximal Leakage , 2019, IEEE Transactions on Information Theory.

[6]  Guy N. Rothblum,et al.  Gentle measurement of quantum states and differential privacy , 2019, Electron. Colloquium Comput. Complex..

[7]  Michael Gastpar,et al.  A New Approach to Adaptive Data Analysis and Learning via Maximal Leakage , 2019, ArXiv.

[8]  Hao Wang,et al.  On the Robustness of Information-Theoretic Privacy Measures and Mechanisms , 2018, IEEE Transactions on Information Theory.

[9]  Oliver Kosut,et al.  Tunable Measures for Information Leakage and Applications to Privacy-Utility Tradeoffs , 2018, IEEE Transactions on Information Theory.

[10]  Sudeep Kamath,et al.  An Operational Approach to Information Leakage , 2018, IEEE Transactions on Information Theory.

[11]  Li Zhou,et al.  Differential Privacy in Quantum Computation , 2017, 2017 IEEE 30th Computer Security Foundations Symposium (CSF).

[12]  Cristian Romero García,et al.  Quantum Machine Learning , 2017, Encyclopedia of Machine Learning and Data Mining.

[13]  Muriel Médard,et al.  From the Information Bottleneck to the Privacy Funnel , 2014, 2014 IEEE Information Theory Workshop (ITW 2014).

[14]  A. Isar,et al.  Open quantum systems , 2004, quant-ph/0411189.

[15]  D. Kaszlikowski,et al.  Iterative procedure for computing accessible information in quantum communication , 2004, quant-ph/0408134.

[16]  R. Schumann Quantum Information Theory , 2000, quant-ph/0010060.

[17]  E. B. Davies,et al.  Information and quantum measurement , 1978, IEEE Trans. Inf. Theory.

[18]  A. Holevo Bounds for the quantity of information transmitted by a quantum communication channel , 1973 .