400 research outputs found
Scalable and Secure Aggregation in Distributed Networks
We consider the problem of computing an aggregation function in a
\emph{secure} and \emph{scalable} way. Whereas previous distributed solutions
with similar security guarantees have a communication cost of , we
present a distributed protocol that requires only a communication complexity of
, which we prove is near-optimal. Our protocol ensures perfect
security against a computationally-bounded adversary, tolerates
malicious nodes for any constant (not
depending on ), and outputs the exact value of the aggregated function with
high probability
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
Dynamic Multiparty Authentication of Data Analytics Services within Cloud Environments
Business analytics processes are often composed from orchestrated,
collaborating services, which are consumed by users from multiple cloud systems
(in different security realms), which need to be engaged dynamically at
runtime. If heterogeneous cloud systems located in different security realms do
not have direct authentication relationships, then it is a considerable
technical challenge to enable secure collaboration. In order to address this
security challenge, a new authentication framework is required to establish
trust amongst business analytics service instances and users by distributing a
common session secret to all participants of a session. We address this
challenge by designing and implementing a secure multiparty authentication
framework for dynamic interaction, for the scenario where members of different
security realms express a need to access orchestrated services. This novel
framework exploits the relationship of trust between session members in
different security realms, to enable a user to obtain security credentials that
access cloud resources in a remote realm. The mechanism assists cloud session
users to authenticate their session membership, thereby improving the
performance of authentication processes within multiparty sessions. We see
applicability of this framework beyond multiple cloud infrastructure, to that
of any scenario where multiple security realms has the potential to exist, such
as the emerging Internet of Things (IoT).Comment: Submitted to the 20th IEEE International Conference on High
Performance Computing and Communications 2018 (HPCC2018), 28-30 June 2018,
Exeter, U
- âŠ