Towards Differential Privacy for Symbolic Systems

TL;DR

This paper introduces a differential privacy method for symbolic control systems that privatizes string data using a Levenshtein automaton, balancing privacy and accuracy.

Contribution

It develops a novel exponential mechanism with a Levenshtein automaton for efficient privacy-preserving string generation in symbolic systems.

Findings

Privacy can be achieved while maintaining accuracy in symbolic data.

The Levenshtein automaton enables efficient privatization of strings.

Numerical results demonstrate the effectiveness of the approach.

Abstract

In this paper, we develop a privacy implementation for symbolic control systems. Such systems generate sequences of non-numerical data, and these sequences can be represented by words or strings over a finite alphabet. This work uses the framework of differential privacy, which is a statistical notion of privacy that makes it unlikely that privatized data will reveal anything meaningful about underlying sensitive data. To bring differential privacy to symbolic control systems, we develop an exponential mechanism that approximates a sensitive word using a randomly chosen word that is likely to be near it. The notion of "near" is given by the Levenshtein distance, which counts the number of operations required to change one string into another. We then develop a Levenshtein automaton implementation of our exponential mechanism that efficiently generates privatized output words. This automaton has letters as its states, and this work develops transition probabilities among these states that give overall output words obeying the distribution required by the exponential mechanism. Numerical results are provided to demonstrate this technique for both strings of English words and runs of a deterministic transition system, demonstrating in both cases that privacy can be provided in this setting while maintaining a reasonable degree of accuracy.