953 research outputs found
Peer-to-Peer Secure Multi-Party Numerical Computation Facing Malicious Adversaries
We propose an efficient framework for enabling secure multi-party numerical
computations in a Peer-to-Peer network. This problem arises in a range of
applications such as collaborative filtering, distributed computation of trust
and reputation, monitoring and other tasks, where the computing nodes is
expected to preserve the privacy of their inputs while performing a joint
computation of a certain function. Although there is a rich literature in the
field of distributed systems security concerning secure multi-party
computation, in practice it is hard to deploy those methods in very large scale
Peer-to-Peer networks. In this work, we try to bridge the gap between
theoretical algorithms in the security domain, and a practical Peer-to-Peer
deployment.
We consider two security models. The first is the semi-honest model where
peers correctly follow the protocol, but try to reveal private information. We
provide three possible schemes for secure multi-party numerical computation for
this model and identify a single light-weight scheme which outperforms the
others. Using extensive simulation results over real Internet topologies, we
demonstrate that our scheme is scalable to very large networks, with up to
millions of nodes. The second model we consider is the malicious peers model,
where peers can behave arbitrarily, deliberately trying to affect the results
of the computation as well as compromising the privacy of other peers. For this
model we provide a fourth scheme to defend the execution of the computation
against the malicious peers. The proposed scheme has a higher complexity
relative to the semi-honest model. Overall, we provide the Peer-to-Peer network
designer a set of tools to choose from, based on the desired level of security.Comment: Submitted to Peer-to-Peer Networking and Applications Journal (PPNA)
200
Energy efficient privacy preserved data gathering in wireless sensor networks having multiple sinks
Wireless sensor networks (WSNs) generally have a many-to-one structure so that event information flows from sensors to a unique sink. In recent WSN applications, many-tomany structures are evolved due to need for conveying collected event information to multiple sinks at the same time. This study proposes an anonymity method bases on k-anonymity for preventing record disclosure of collected event information in WSNs. Proposed method takes the anonymity requirements of multiple sinks into consideration by providing different levels of privacy for each destination sink. Attributes, which may identify of an event owner, are generalized or encrypted in order to
meet the different anonymity requirements of sinks. Privacy guaranteed event information can be multicasted to all sinks instead of sending to each sink one by one. Since minimization of energy consumption is an important design criteria for WSNs, our method enables us to multicast the same event information
to multiple sinks and reduce energy consumption
From usability to secure computing and back again
Secure multi-party computation (MPC) allows multiple parties
to jointly compute the output of a function while preserving
the privacy of any individual party’s inputs to that function.
As MPC protocols transition from research prototypes to realworld
applications, the usability of MPC-enabled applications
is increasingly critical to their successful deployment and
widespread adoption. Our Web-MPC platform, designed with
a focus on usability, has been deployed for privacy-preserving
data aggregation initiatives with the City of Boston and the
Greater Boston Chamber of Commerce. After building and
deploying an initial version of the platform, we conducted a
heuristic evaluation to identify usability improvements and
implemented corresponding application enhancements. However,
it is difficult to gauge the effectiveness of these changes
within the context of real-world deployments using traditional
web analytics tools without compromising the security guarantees
of the platform. This work consists of two contributions
that address this challenge: (1) the Web-MPC platform has
been extended with the capability to collect web analytics
using existing MPC protocols, and (2) as a test of this feature
and a way to inform future work, this capability has been
leveraged to conduct a usability study comparing the two versions
ofWeb-MPC. While many efforts have focused on ways
to enhance the usability of privacy-preserving technologies,
this study serves as a model for using a privacy-preserving
data-driven approach to evaluate and enhance the usability of
privacy-preserving websites and applications deployed in realworld
scenarios. Data collected in this study yields insights
into the relationship between usability and security; these can
help inform future implementations of MPC solutions.Published versio
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