  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.
