Neural Information Processing Systems http://nips.cc/

Despite its pilot progress, personalized federated learning with differential privacy still faces numerous challenges.

Wepropose twoinnovativealgorithms, DP-GLMtron and DP-TAGLMtron, that outperform the conventional DPSGD. Inlight ofthevast quantities of personal and sensitiveinformation involved, traditional methods of ensuring privacy are encountering significant challenges.

Neural Information Processing Systems http://nips.cc/

When learning from sensitive data, care must be taken to ensure that training algorithms address privacy concerns. The canonical Private Aggregation of Teacher Ensembles, or P A TE, computes output labels by aggregating the predictions of a (possibly distributed) collection of teacher models via a voting mechanism. The mechanism adds noise to attain a differential privacy guarantee with respect to the teachers' training data. In this work, we observe that this use of noise, which makes P A TE predictions stochastic, enables new forms of leakage of sensitive information. For a given input, our adversary exploits this stochasticity to extract high-fidelity histograms of the votes submitted by the underlying teachers. From these histograms, the adversary can learn sensitive attributes of the input such as race, gender, or age. Although this attack does not directly violate the differential privacy guarantee, it clearly violates privacy norms and expectations, and would not be possible at all without the noise inserted to obtain differential privacy. In fact, counter-intuitively, the attack becomes easier as we add more noise to provide stronger differential privacy. We hope this encourages future work to consider privacy holistically rather than treat differential privacy as a panacea.

Therefore, if ν() < +, then we can bound(10) with eαν(). To avoid crowded notations, we drop the conditioning onz from Pr[ |ρ = z]. The issue is how to proceed. Let φ be the standard normal density function andΦ be the CDF. The algorithm using SVT suchthat itonly releases the private answerstothe queries if the answer is sufficiently different from the "guess".

Machine learning (ML) models are known to leak information about their training data.