229 research outputs found
Privacy preserving distributed optimization using homomorphic encryption
This paper studies how a system operator and a set of agents securely execute
a distributed projected gradient-based algorithm. In particular, each
participant holds a set of problem coefficients and/or states whose values are
private to the data owner. The concerned problem raises two questions: how to
securely compute given functions; and which functions should be computed in the
first place. For the first question, by using the techniques of homomorphic
encryption, we propose novel algorithms which can achieve secure multiparty
computation with perfect correctness. For the second question, we identify a
class of functions which can be securely computed. The correctness and
computational efficiency of the proposed algorithms are verified by two case
studies of power systems, one on a demand response problem and the other on an
optimal power flow problem.Comment: 24 pages, 5 figures, journa
Private Computation of Polynomials over Networks
This study concentrates on preserving privacy in a network of agents where
each agent seeks to evaluate a general polynomial function over the private
values of her immediate neighbors. We provide an algorithm for the exact
evaluation of such functions while preserving privacy of the involved agents.
The solution is based on a reformulation of polynomials and adoption of two
cryptographic primitives: Paillier as a Partially Homomorphic Encryption scheme
and multiplicative-additive secret sharing. The provided algorithm is fully
distributed, lightweight in communication, robust to dropout of agents, and can
accommodate a wide class of functions. Moreover, system theoretic and secure
multi-party conditions guaranteeing the privacy preservation of an agent's
private values against a set of colluding agents are established. The
theoretical developments are complemented by numerical investigations
illustrating the accuracy of the algorithm and the resulting computational
cost.Comment: 11 pages, 2 figure
Private Computation of Polynomials over Networks
This study concentrates on preserving privacy in a network of agents where
each agent seeks to evaluate a general polynomial function over the private
values of her immediate neighbors. We provide an algorithm for the exact
evaluation of such functions while preserving privacy of the involved agents.
The solution is based on a reformulation of polynomials and adoption of two
cryptographic primitives: Paillier as a Partially Homomorphic Encryption scheme
and multiplicative-additive secret sharing. The provided algorithm is fully
distributed, lightweight in communication, robust to dropout of agents, and can
accommodate a wide class of functions. Moreover, system theoretic and secure
multi-party conditions guaranteeing the privacy preservation of an agent's
private values against a set of colluding agents are established. The
theoretical developments are complemented by numerical investigations
illustrating the accuracy of the algorithm and the resulting computational
cost.Comment: 11 pages, 2 figure
SIG-DB: leveraging homomorphic encryption to Securely Interrogate privately held Genomic DataBases
Genomic data are becoming increasingly valuable as we develop methods to
utilize the information at scale and gain a greater understanding of how
genetic information relates to biological function. Advances in synthetic
biology and the decreased cost of sequencing are increasing the amount of
privately held genomic data. As the quantity and value of private genomic data
grows, so does the incentive to acquire and protect such data, which creates a
need to store and process these data securely. We present an algorithm for the
Secure Interrogation of Genomic DataBases (SIG-DB). The SIG-DB algorithm
enables databases of genomic sequences to be searched with an encrypted query
sequence without revealing the query sequence to the Database Owner or any of
the database sequences to the Querier. SIG-DB is the first application of its
kind to take advantage of locality-sensitive hashing and homomorphic encryption
to allow generalized sequence-to-sequence comparisons of genomic data.Comment: 38 pages, 3 figures, 4 tables, 1 supplemental table, 7 supplemental
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