Verifiable Differential Privacy For When The Curious Become Dishonest. (arXiv:2208.09011v1 [cs.CR])

Many applications seek to produce differentially private statistics on
sensitive data. Traditional approaches in the centralised model rely on a
trusted aggregator to gather the raw data, aggregate statistics and introduce
appropriate noise. Recent work has tried to relax the trust assumptions and
reduce the need for trusted entities. However, such systems can trade off trust
for increased noise and still require complete trust in some participants.
Moreover, they do not prevent a malicious entity from introducing adversarial
noise to …

differential privacy privacy