8,234 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
A Flexible Privacy-preserving Framework for Singular Value Decomposition under Internet of Things Environment
The singular value decomposition (SVD) is a widely used matrix factorization
tool which underlies plenty of useful applications, e.g. recommendation system,
abnormal detection and data compression. Under the environment of emerging
Internet of Things (IoT), there would be an increasing demand for data analysis
to better human's lives and create new economic growth points. Moreover, due to
the large scope of IoT, most of the data analysis work should be done in the
network edge, i.e. handled by fog computing. However, the devices which provide
fog computing may not be trustable while the data privacy is often the
significant concern of the IoT application users. Thus, when performing SVD for
data analysis purpose, the privacy of user data should be preserved. Based on
the above reasons, in this paper, we propose a privacy-preserving fog computing
framework for SVD computation. The security and performance analysis shows the
practicability of the proposed framework. Furthermore, since different
applications may utilize the result of SVD operation in different ways, three
applications with different objectives are introduced to show how the framework
could flexibly achieve the purposes of different applications, which indicates
the flexibility of the design.Comment: 24 pages, 4 figure
Privacy-Friendly Collaboration for Cyber Threat Mitigation
Sharing of security data across organizational boundaries has often been
advocated as a promising way to enhance cyber threat mitigation. However,
collaborative security faces a number of important challenges, including
privacy, trust, and liability concerns with the potential disclosure of
sensitive data. In this paper, we focus on data sharing for predictive
blacklisting, i.e., forecasting attack sources based on past attack
information. We propose a novel privacy-enhanced data sharing approach in which
organizations estimate collaboration benefits without disclosing their
datasets, organize into coalitions of allied organizations, and securely share
data within these coalitions. We study how different partner selection
strategies affect prediction accuracy by experimenting on a real-world dataset
of 2 billion IP addresses and observe up to a 105% prediction improvement.Comment: This paper has been withdrawn as it has been superseded by
arXiv:1502.0533
On content-based recommendation and user privacy in social-tagging systems
Recommendation systems and content filtering approaches based on annotations and ratings, essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised content. This activity, in which users engage collectively has been named social tagging, and it is one of the most popular in which users engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. It, in fact, consists of describing online or offline resources by using free-text labels (i.e. tags), therefore exposing the user profile and activity to privacy attacks. Users, as a result, may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy enhancing technology consisting of generating tags for categories or resources that do not reflect the user's actual preferences. By modifying their profile, tag forgery may have a negative impact on the quality of the recommendation system, thus protecting user privacy to a certain extent but at the expenses of utility loss. The impact of tag forgery on content-based recommendation is, therefore, investigated in a real-world application scenario where different forgery strategies are evaluated, and the consequent loss in utility is measured and compared.Peer ReviewedPostprint (author’s final draft
- …