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Found 2 papers, 1 papers with code
Quantifying identifiability to choose and audit $ε$ in differentially private deep learning
We transform $(\epsilon,\delta)$ to a bound on the Bayesian posterior belief of the adversary assumed by differential privacy concerning the presence of any record in the training dataset.
AggFT: Low-Cost Fault-Tolerant Smart Meter Aggregation with Proven Termination and Privacy
We revisit an existing error-resilient privacy-preserving aggregation protocol based on masking and improve it by: (i) performing changes in the cryptographic parts that lead to a reduction of computational costs, (ii) simplifying the behaviour of the protocol in the presence of faults, and showing a proof of proper termination under a well-defined failure model, (iii) decoupling the computation part from the data flow so that the algorithm can also be used with homomorphic encryption as a basis for privacy-preservation.
Cryptography and Security
