SuperDP refutes ε-DP via simultaneous synthesis of input pairs and witness functions using upper expectation supermartingales and lower expectation submartingales, delivering the first fully automated, sound, and semi-complete method applicable to both discrete and continuous stochastic mechanisms.
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A scalable probabilistic round-off analysis applies concentration inequalities to over-approximated Taylor expansions from FPTaylor, yielding orders-of-magnitude speedups with comparable precision.
citing papers explorer
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SuperDP: Differential Privacy Refutation via Supermartingales
SuperDP refutes ε-DP via simultaneous synthesis of input pairs and witness functions using upper expectation supermartingales and lower expectation submartingales, delivering the first fully automated, sound, and semi-complete method applicable to both discrete and continuous stochastic mechanisms.
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Probabilistic Floating-Point Round-Off Analysis via Concentration Inequalities
A scalable probabilistic round-off analysis applies concentration inequalities to over-approximated Taylor expansions from FPTaylor, yielding orders-of-magnitude speedups with comparable precision.