11,445 research outputs found
Privacy-preserving distributed service recommendation based on locality-sensitive hashing
With the advent of IoT (Internet of Things) age, considerable web services are emerging rapidly in service communities, which places a heavy burden on the target users’ service selection decisions. In this situation, various techniques, e.g., collaborative filtering (i.e., CF) is introduced in service recommendation to alleviate the service selection burden. However, traditional CF-based service recommendation approaches often assume that the historical user-service quality data is centralized, while neglect the distributed recommendation situation. Generally, distributed service recommendation involves inevitable message communication among different parties and hence, brings challenging efficiency and privacy concerns. In view of this challenge, a novel privacy-preserving distributed service recommendation approach based on Locality-Sensitive Hashing (LSH), i.e., DistSRLSH is put forward in this paper. Through LSH, DistSRLSH can achieve a good tradeoff among service recommendation accuracy, privacy-preservation and efficiency in distributed environment. Finally, through a set of experiments deployed on WS-DREAM dataset, we validate the feasibility of our proposal in handling distributed service recommendation problems
Peer-to-Peer Secure Multi-Party Numerical Computation
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 numerous other tasks, where the computing nodes
would like 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 examine several possible approaches and discuss their feasibility. Among the
possible approaches, we identify a single approach which is both scalable and
theoretically secure.
An additional novel contribution is that we show how to compute the
neighborhood based collaborative filtering, a state-of-the-art collaborative
filtering algorithm, winner of the Netflix progress prize of the year 2007. Our
solution computes this algorithm in a Peer-to-Peer network, using a privacy
preserving computation, without loss of accuracy.
Using extensive large scale simulations on top of real Internet topologies,
we demonstrate the applicability of our approach. As far as we know, we are the
first to implement such a large scale secure multi-party simulation of networks
of millions of nodes and hundreds of millions of edges.Comment: 10 pages, 2 figures, appeared in the 8th IEEE Peer-to-Peer Computing,
Aachen, Germany, Sept. 200
Enhancing privacy and preserving accuracy of a distributed collaborative filtering
Collaborative Filtering (CF) is a powerful technique for generating personalized predictions. CF systems are typically based on a central storage of user profiles used for generating the recommendations. However, such centralized storage introduces a severe privacy breach, since the profiles may be accessed for purposes, possibly malicious, not related to the recommendation process. Recent researches proposed to protect the privacy of CF by distributing the profiles between multiple repositories and exchange only a subset of the profile data, which is useful for the recommendation. This work investigates how a decentralized distributed storage of user profiles combined with data modification techniques may mitigate some privacy issues. Results of experimental evaluation show that parts of the user profiles can be modified without hampering the accuracy of CF predictions. The experiments also indicate which parts of the user profiles are most useful for generating accurate CF predictions, while their exposure still keeps the essential privacy of the users
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
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
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