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A Recommendation-based Matchmaking Scheme for Mult
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Mobile social networks (MSNs) enable users to discover and interact with existing and potential friends both in the cyberspace and in the real world. Although mobile social network applications bring us much convenience, privacy concerns become the key security issue hindering their wide adoptions. In this paper, we propose a recommendationbased matchmaking scheme in multiple MSNs, which can help users to find their potential friends without disclosing their private information. The correctness and security analyzing results show that our scheme can resist both semihonest and malicious attacks while providing matchmaking functionality against private data leakage.
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A survey on decentralized Online Social Networks
no vote
Because of growing popularity of Online Social Networks (SONs) and huge amount of sensitive shared data, preserving privacy is becoming a major issue for OSN users. While mostONs rely on a centralized architecture, with an omnipotent Service Provider, several decentralized architectures have recently been proposed for decentralized OSNs (DOWNs).In this work, we present a survey of existing proposals. We propose a classification of previous work under two dimensions: (i) types of approaches with respect to resource provisioning devices and (ii) adopted strategies for three main technical issues for DOSN(decentralizing storage of content, access control and interaction/signaling). We point out advantages and limitations of each approach and conclude with a discussion on the impact of SNs on users, ON providers and other stakeholders.
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Adaptive content recommendation for mobile users:
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human-centric framework for context-aware flowable
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A mobile social network for efficient contents sha
4.0
In this paper, we focus on constructing a mobile social network over a mobile ad hocnetwork. Although some mobile social networks have been proposed to address contents sharing and search in mobile ad hoc networks, most existing methods incur either high network management overheads or low efficiency of contents search. The proposed method constructs a mobile social network according to user interests and location information. Since the mobile nodes with nearby positions are linked together, the network management overhead of the proposed method is decreased. The contents search can be performed by using the constructed social links with low communication cost and high success rate. The various experimental results based on the synthetic datasets and real datasets have showed that the proposed method has a great advantage comparing to existing methods.
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Improved Signal-to-Noise Ratio Estimation for Spee
no vote
This paper addresses the problem of single-microphone speech enhancement in noisy environments. State-of-the-art short-time noise reduction techniques are most often expressed as a spectral gain depending on the signal-to-noise ratio (SNR). The well-known decision-directed (DD) approach drastically limits the level of musical noise, but the estimated a priori SNR is biased since it depends on the speech spectrum estimation in the previous frame. Therefore, the gain function matches the previous frame rather than the current one which degrades the noise reduction performance. The consequence of this bias is an annoying reverberation effect. We propose a method called two-step noise reduction (TSNR) technique which solves this problem while maintaining the benefits of the decision-directed approach. The estimation of the a priori SNR is refined by a second step to remove the bias of the DD approach, thus removing the r
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A TWO-STEP NOISE REDUCTION TECHNIQUE
no vote
This paper addresses the problem of single microphone speech enhancement in noisy environments. Common short-time noise reduction techniques proposed in the art are expressed as a spectral gain depending on the a priori SNR. In the well-known decisiondirected approach, the a priori SNR depends on the speech spectrum estimation in the previous frame. As a consequence the gain function matches the previous frame rather than the current one which degrades the noise reduction performance. We propose a new method called Two-Step Noise Reduction (TSNR) technique which solves this problem while maintaining the benefits of the decision-directed approach. This method is analyzed and results in voice communication and speech recognition context are given.
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