17887061
Popularity Biased Hybrid Search in P2P Systems
Hybrid search, which combines the flooding and DHT techniques, has been employed for efficient content-locating in peer-to-peer (P2P) systems. Choosing a flooding or a DHT subjects to the population of desired data. However, existing studies summarizes the popularity only based on local information, or does not take into account the dynamic factor of P2P systems. Thus, the users suffer from long search latency when performing hybrid search due to inaccurate estimations of the resource popularity. In this paper, we propose a popularity biased hybrid search method in systems. Our model allows the P2P systems dynamically detect the content popularity, and accurately make a decision of search methods. In this way, our work effectively reduces the query traffic cost and response time. The result our trace-driven simulations report shows that our model outperforms existing approaches in search efficiency
27160507
A Random Walk Based Anonymous Peer-to-Peer Protocol Design
Anonymity has been one of the most challenging issues in Ad Hoc environment such as P2P systems. In this paper, we propose an anonymous protocol called Random Walk based Anonymous Protocol (RWAP), in decentralized P2P systems. We evaluate RWAP by comprehensive trace driven simulations. Results show that RWAP significantly reduces traffic cost and encryption overhead compared with existing approaches.
40646697
HAND: An Overlay Optimization Algorithm in Peer-to-Peer Systems
Recently, peer-to-peer (P2P) model becomes popular due to its outstanding resource sharing ability in large-scale distributed systems. However, the most popular P2P system – unstructured P2P system, suffers greatly from the mismatching between the logical overlay and physical topology. This mismatching problem causes a large volume of redundant traffic, which makes peer-to-peer system far from scalable and degrades their search efficiency dramatically. In this paper we address the mismatching problem in an unstructured P2P architecture by developing a distributed overlay optimizing algorithm – HAND (Hops Adaptive Neighbor Discovery). Through comprehensive simulations, we show that HAND significantly outperforms previous approaches, especially in large-scale P2P systems.
6112726
Pseudo Trust: Zero-Knowledge Based Authentication in Anonymous Peer-to-Peer Protocols
Most of the current trust models in peer-to-peer (P2P) systems are identity based, which means that in order for one peer to trust another, it needs to know the other peer's identity. Hence, there exists an inherent tradeoff between trust and anonymity. To the best of our knowledge, there is currently no P2P protocol that provides complete mutual anonymity as well as authentication and trust management. We propose a zero-knowledge authentication scheme called pseudo trust (PT), where each peer, instead of using its real identity, generates an unforgeable and verifiable pseudonym using a one-way hash function. A novel authentication scheme based on zero-knowledge proof is designed so peers can be authenticated without leaking any sensitive information. With the help of PT, most existing identity-based trust management schemes become applicable in mutual anonymous P2P systems. We analyze the levels of security and anonymity in PT, and evaluate its performance using trace-driven simulations and a prototype implementation. The strengths of pseudo trust include the lack of need for a centralized trusted party or CA, high scalability and security, low traffic and cryptography processing overheads, and man-in-middle attack resistance. We aim for the pseudo trust design to be included in the P2P trust and anonymity context.
15978422
TruGrid: a self-sustaining trustworthy grid
In typical distributed environments, there are two parties: consumers and providers. Consumers have computational jobs while may lack of computational resources. Providers have relatively underutilized resources. With the rapid advancement in high-speed networks, how to enable the effective and secure interaction between consumers and providers has become increasingly important. Because of the distributed ownership of resources, however, it is a very challenging problem. We propose the TruGrid, a self-sustaining trustworthy grid, to solve it. The TruGrid is a novel loosely connected grid system, which guarantees the incentive of every participant and therefore it is a self-sustaining system. We also identify two security challenges arising in the TruGrid, i.e., free-rider and boaster. The proposed trustworthiness management and the penalty model successfully overcome the challenges. Simulation results demonstrate that the TruGrid is a promising actuator to enable the interaction between autonomous consumers and providers.
163835
Popularity Adaptive Search in Hybrid P2P Systems
In a hybrid peer-to-peer (P2P) system, flooding and DHT are both employed for content locating. The decision to use flooding or DHT largely depends on the population of desired data. Previous works either use local information only, or do not consider dynamic factors of P2P systems. In this paper, we propose a popularity adaptive search method for hybrid (PASH) P2P systems. By dynamically detecting the content popularity, PASH properly selects search methods and efficiently saves query traffic cost and response time. We comprehensively evaluate PASH through synthetic and trace-driven simulations. The results show that PASH outperforms existing approaches and it also scales well.
21192
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist a large number of patterns and/or long patterns.In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a condensed, smaller data structure, FP-tree which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern-fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent-pattern mining methods.
6059661
Mining frequent patterns without candidate generation
Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns.
In this study, we propose a novel frequent pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree-based mining method, FP-growth, for mining the complete set of frequent patterns by pattern fragment growth. Efficiency of mining is achieved with three techniques: (1) a large database is compressed into a highly condensed, much smaller data structure, which avoids costly, repeated database scans, (2) our FP-tree-based mining adopts a pattern fragment growth method to avoid the costly generation of a large number of candidate sets, and (3) a partitioning-based, divide-and-conquer method is used to decompose the mining task into a set of smaller tasks for mining confined patterns in conditional databases, which dramatically reduces the search space. Our performance study shows that the FP-growth method is efficient and scalable for mining both long and short frequent patterns, and is about an order of magnitude faster than the Apriori algorithm and also faster than some recently reported new frequent pattern mining methods.
10015971
Predicting Change Impact in Architecture Design with Bayesian Belief Networks
Research into design rationale in the past has focused on the representation of reasons and has omitted the connections between design rationales and design artefacts. Without such connections, designers and architects cannot easily assess how changing requirements or designs may affect the system. In this paper, we introduce a model called Architecture Rationale and Element Linkage (AREL) to represent the causal relationship between architecture elements and decisions. We further model AREL as a Bayesian Belief Network (BBN) to capture the probabilistic relationships between architecture elements and decisions in an architecture design model. Such probabilistic modelling enables architects to quantitatively predict and diagnose impact of change when part of the requirements or designs are changing. Using a partial design of a cheque image processing system, we illustrate how AREL is used to represent the decision model and how BBN is used to predict and diagnose change in the architecture design. We use a UML tool to capture the AREL model and a BBN tool to compute the probabilities of change impact.
43402198
Mining Closed Relational Graphs with Connectivity Constraints
Relational graphs are widely used in modeling large scale networks such as biological networks and social networks. In a relational graph, each node represents a distinct entity while each edge represents a relationship between entities. Various algorithms were developed to discover interesting patterns from a single relational graph (Z. Wu et al., 1993). However, little attention has been paid to the patterns that are hidden in multiple relational graphs. One interesting pattern in relational graphs is frequent highly connected subgraph which can identify recurrent groups and clusters. In social networks, this kind of pattern corresponds to communities where people are strongly associated. For example, if several researchers co-author some papers, attend the same conferences, and refer their works from each other, it strongly indicates that they are studying the same research theme.
1659291
A Software Engineering Perspective for Services Security
Services are usually developed and deployed independently; and systems can be formed by composing relevant services to achieve set goals. In such an open and dynamic environment, security is of paramount importance. We have seen much work in the traditional area of information and network security, focusing on developing various security techniques. More recently, there have been efforts in integrating the security techniques into languages and infrastructural support that are used for developing services and systems. In fact, the development of services and the composition of service-based systems are software engineering activities. As such, they need to be viewed from a software engineering perspective. In this paper, we introduce an approach to services security engineering, to answer the questions like what the security properties of services and service-based systems are and how they meet the user’s security requirements. It deals with the issues of (1) security property characterisation for services, (2) compositional security analysis for service-based systems, and (3) certification of services.