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Differential Privacy for Symbolic Systems with Application to Markov Chains by Chen, Bo; Leahy, Kevin; Jones, Austin; Hale, Matthew is a scholarly article available to read on EtoBox.

What is Differential Privacy for Symbolic Systems with Application to Markov Chains about?

Data-driven systems are gathering increasing amounts of data from users, and sensitive user data requires privacy protections. In some cases, the data gathered is non-numerical or symbolic, and conventional approaches to privacy, e.g., adding noise, do not apply, though such systems still require privacy protections. Accordingly, we present a novel differential privacy framework for protecting trajectories generated by symbolic systems. These trajectories can be represented as words or strings over a finite alphabet. We develop new differential privacy mechanisms that approximate a sensitive word using a random word that is likely to be near it. An offline mechanism is implemented efficiently using a Modified Hamming Distance Automaton to generate whole privatized output words over a finite time horizon. Then, an online mechanism is implemented by taking in a sensitive symbol and generating a randomized output symbol at each timestep. This work is extended to Markov chains to generate differentially private state sequences that a given Markov chain could have produced. Statistical accuracy bounds are developed to quantify the accuracy of these mechanisms, and numerical results vali

Author

Chen, Bo; Leahy, Kevin; Jones, Austin; Hale, Matthew

Published

2022

Language

EN