Taking a Lesson from Quantum Particles for Statistical Data Privacy
This work addresses privacy vulnerabilities in AI for data protection, offering a novel approach that is incremental by building on existing entropy-based methods.
The paper tackles the problem of data privacy by extending an information-theoretic framework using Fisher information to avoid assumptions on private data, resulting in an optimal noise method derived from Schrödinger's equation and proving a privacy-utility trade-off akin to the Heisenberg uncertainty principle.
Privacy is under threat from artificial intelligence revolution fueled by unprecedented abundance of data. Differential privacy, an established candidate for privacy protection, is susceptible to adversarial attacks, acts conservatively, and leads to miss-implementations because of lacking systematic methods for setting its parameters (known as the privacy budget). An alternative is information-theoretic privacy using entropy with the drawback of requiring prior distribution of the private data. Here, by using the Fisher information, information-theoretic privacy framework is extended to avoid unnecessary assumptions on the private data. The optimal privacy-preserving additive noise, extracted by minimizing the Fisher information, must follow the time-independent Schrodinger's equation. A fundamental trade-off between privacy and utility is also proved, reminiscent of the Heisenberg uncertainty principle.