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Taking a Lesson from Quantum Particles for Statistical Data Privacy
[article]
2019
arXiv
pre-print
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
arXiv:1908.04954v1
fatcat:grdmmsmbdzcbhftkqyi4ivqlwu