Privacy-aware quadratic optimization using partially homomorphic encryption

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.

Implementing homomorphic encryption based secure feedback control

“…Homomorphic encryption has been used previously for third-party cloud-computing services [1,17,20,32,35,60]. More recent studies [16,25,27,28,50] have considered challenges associated with the use of homomorphic encryption in closed-loop control of physical systems, such as maintaining stability and performance, albeit without considering timing concerns (by not getting into the computational time of encryption, computation, and decryption and assuming all underlying computations are instantaneous). None of these studies consider dynamic control laws; they are all restricted to static control laws without any form of memory.…”

Solving bin-packing problems under privacy preservation: Possibilities and trade-offs

“…Since then, a large number of privacy protection mechanisms have been proposed in [12,17,29,30,45,46] . These works can generally be divided into two categories: the ones using cryptographic tools [2,3,9,12,17,20,29,30,34,50] and the ones using perturbation based techniques [45,46] .…”

“…The works using cryptographic tools mostly have a multi-party communication setting, either using secret sharing [29,30,39] or homomorphic encryption [2,3,9,17,20,50] to preserve privacy. While such methods achieve solutions of similar utility to those in the non-private setting, they do so at the cost of increased computation time.…”

Protecting the grid topology and user consumption patterns during state estimation in smart grids based on data obfuscation

“…Obfuscation (Shoukry et al 2016) is the procedure of transforming the data into masked data through randomization and performing the necessary operations on this masked obfuscated data. The obfuscated data can be unmasked by inverting the randomized transformation using the respective private keys.…”