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Per-se Privacy Preserving Distributed Optimization
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0001-9810-3478
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(English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523Article in journal (Other academic) Submitted
Abstract [en]

Ensuring privacy in distributed optimization is essential in many contexts, such as healthcare data,banks, e-commerce, government services, and social networks. In these contexts, it is common thatdifferent parties coordinate to solve a specific optimization problem whose data is dispersed amongthe parties, where no entity wants to publish its data during the solution procedure. Addressing theseproblems falls under the umbrella of well-knownsecured multiparty computation(SMC). Existingapproaches for SMC are mostly based on cryptography. Surprisingly, little attention has been devotedthus far to develop non-cryptographic approaches, that can be much more efficient. In this paper,we investigate alternative non-cryptographic methods based onmathematical optimization techniques.First, aunified frameworkto encapsulate existing non-cryptographic methods, which rely algebraictransformations to disguise sensitive problem data, is developed. The proposed framework capitalizes onkey optimization techniques, such aschange of variablesandtransformation of objective and constraintfunctions, for equivalent problem formation. Moreover, the privacy preserving properties that are inherentin the mathematical optimization techniques, including classical decomposition methods (e.g., primal anddual decomposition), and state-of-the-art methods, such as alternating direction method of multipliersare investigated. A general definition for quantifying the privacy in the context of non-cryptographicapproaches is proposed. A number of examples are provided to illustrate the importance of our proposedalgorithms. It is concluded that the theory is in its infancy and that huge benefits can be achieved by asubstantial development.

Place, publisher, year, edition, pages
IEEE.
Keyword [en]
Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-138496OAI: oai:DiVA.org:kth-138496DiVA: diva2:681121
Note

QS 2014

Available from: 2013-12-19 Created: 2013-12-19 Last updated: 2017-12-06Bibliographically approved

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Fischione, Carlo

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