Privacy-aware quadratic optimization using partially homomorphic encryption [Conference Paper]

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.

External paper URL

Publication Forum: 2016 IEEE 55th Conference on Decision and Control (CDC)

Date: 2016-12-12

Publisher: IEEE

NESL Document?: Yes

Document category: Conference Paper

Primary Research Area: Privacy, Security, and Integrity