NESL Technical Report #: 2016-12-12
Abstract: We consider a problem where multiple agents participate in solving a quadratic optimization problem subject to linear inequality constraints in a privacy-preserving manner. Several variables of the objective function as well as the constraints are privacy-sensitive and are known to different agents. We propose a privacy-preserving protocol based on partially homomorphic encryption where each agent encrypts its own information before sending it to an untrusted cloud computing infrastructure. To find the optimal solution the cloud applies a gradient descent algorithm on the encrypted data without the ability to decrypt it. The privacy of the proposed protocol against coalitions of colluding agents is analyzed using the cryptography notion of zero-knowledge proofs.
Publication Forum: 2016 IEEE 55th Conference on Decision and Control (CDC)
NESL Document?: Yes
Document category: Conference Paper
Primary Research Area: #<ResearchArea:0x007f72f8d0aa80>