… We demonstrate that our proposed algorithm can achieve client level differential privacy at …
client level-dp. Experimental setups however differ and [6] also includes element-level privacy …

… Although we can improve privacy and achieve clientlevel DP in FL by adding Gaussian
noise locally and using secure aggregation, the resulting accuracy of the trained model is often …

… Theorem 2, below, shows that nothing can be gained by using computational differential
privacy rather than statistical differential privacy, as long as we consider mechanisms whose …

… privacy notion of differential privacy with FL. To guarantee the client-level differential privacy
in FL algorithms, the clients’ … have to be clipped before adding privacy noise. Such clipping …

… Personalised Differential Privacy This paper addresses … of differential privacy called
personalised differential privacy (PDP) which permits each individual to have a personalised privacy …

… concept of differential privacy (DP), in which artificial noise is added to parameters at the
clients’ … First, we prove that the NbAFL can satisfy DP under distinct protection levels by properly …

… privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of
differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We …

… free from privacy and … differential Privacy (DP) to protect both privacy and robustness in FL.
To this end, we present a first-of-its-kind evaluation of Local and Central Differential Privacy (…

… the original clients into more intermediaries achieves DP with the same privacy budget and
… that when sample-level DP and client-level DP have equivalent noise levels, the variance of …

… to tackle the privacy issues in deep and federated learning (FL). Particularly, we focus on
differential privacy (DP) which became a de facto standard for protecting users’ privacy in …