Efficient Verifiable Differential Privacy with Input Authenticity in the Local and Shuffle Model

ePrint Report: Efficient Verifiable Differential Privacy with Input Authenticity in the Local and Shuffle Model

Tariq Bontekoe, Hassan Jameel Asghar, Fatih Turkmen

Local differential privacy (LDP) is an efficient solution for providing privacy to client's sensitive data while simultaneously releasing aggregate statistics without relying on a trusted central server (aggregator) as in the central model of differential privacy. The shuffle model with LDP provides an additional layer of privacy, by disconnecting the link between clients and the aggregator, further improving …

authenticity client data differential privacy eprint report input local privacy report sensitive sensitive data shuffle solution statistics