1164311
Trust and reputation model in peer-to-peer networks
It is important to enable peers to represent and update their trust in other peers in open networks for sharing files, and especially services. We propose a Bayesian network-based trust model and a method for building reputation based on recommendations in peer-to-peer networks. Since trust is multifaceted, peers need to develop differentiated trust in different aspects of other peers' capability. The peer's needs are different in different situations. Depending on the situation, a peer may need to consider its trust in a specific aspect of another peer's capability or in multiple aspects. Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust. The evaluation of the model using a simulation shows that the system where peers communicate their experiences (recommendations) outperforms the system where peers do not share recommendations with each other and that a differentiated trust adds to the performance in terms of percentage of successful interactions.
11471646
Bayesian network-based trust model
We propose a Bayesian network-based trust model. Since trust is multifaceted, even in the same context, agents still need to develop differentiated trust in different aspects of other agents' behaviors. The agent's needs are different in different situations. Depending on the situation, an agent may need to consider its trust in a specific aspect of another agent's capability or in a combination of multiple aspects. Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust. A Bayesian network-based trust model is presented for a file sharing peer-to-peer application.
8204880
Privacy aware data generation for testing database applications
Testing of database applications is of great importance. A significant issue in database application testing consists in the availability of representative data. In this paper, we investigate the problem of generating a synthetic database based on a-priori knowledge about a production database. Our approach is to fit general location model using various characteristics (e.g., constraints, statistics, rules) extracted from the production database and then generate the synthetic data using model learnt. The generated data is valid and similar to real data in terms of statistical distribution, hence it can be used for functional and performance testing. As characteristics extracted may contain information which may be used by attacker to derive some confidential information about individuals, we present our disclosure analysis method which applies cell suppression technique for identity disclosure analysis and perturbation for value disclosure.
14047084
Efficient Algorithm for Embedding Hypergraphs in a Cycle
The problem of Minimum Congestion Hypergraph Embedding in a Cycle (MCHEC) is to embed the hyperedges of a hypergraph as paths in a cycle such that the maximum congestion (the maximum number of paths that use any single link in the cycle) is minimized. This problem has many applications, including minimizing communication congestions in computer networks and parallel computations. The MCHEC problem is NP-hard. We give a 1.8-approximation algorithm for the problem. This improves the previous 2-approximation results. The algorithm has the optimal O(mn) time for the hypergraph with m hyperedges and n nodes.
2865514
MARio: mobility-adaptive routing using route lifetime abstractions in mobile ad hoc networks
Mobile Ad hoc NETworks (MANETs) often have fast-changing and unpredictable mobility. This is especially true for recently emerging mobile sensor networks [7] and delay tolerant networks [2], where extreme network conditions are prevalent. To better adjust to such network conditions, we propose to deconstruct the underlying node mobility into a set of abstractions that can be readily leveraged for driving dynamic routing decisions. In particular, we investigate the feasibility of using route lifetime to abstract mobility. Through extensive simulations on DSR, we demonstrate up to 31% improvement in packet delivery rate and 53% improvement in average end-to-end packet latency, using our proposed route prefetch and decay techniques that leverage knowledge of route lifetimes.
3032688
Statistical Identification of Key Phrases for Text Classification
Algorithms for text classification generally involve two stages, the first of which aims to identify textual elements (words and/or phrases) that may be relevant to the classification process. This stage often involves an analysis of the text that is both language-specific and possibly domain-specific, and may also be computationally costly. In this paper we examine a number of alternative keyword-generation methods and phrase-construction strategies that identify key words and phrases by simple, language-independent statistical properties. We present results that demonstrate that these methods can produce good classification accuracy, with the best results being obtained using a phrase-based approach.
8818017
Using Model Trees for Classification
Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5′, based on Quinlan's M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.