26868655
Complexity of the generalized conjugacy problem
Recently, the generalized conjugacy problem(GCP) in braid groups was introduced as a candidate for cryptographic one-way function. A GCP in a braid group can be transformed into a GCP in a general linear group by the Burau representation. We study the latter problem induced in this way.
8508201
Cryptanalysis of Some Group-Oriented Proxy Signature Schemes
A proxy signature scheme allows an entity to delegate its signing power to another entity. Since the notion of proxy signatures was first introduced, many proxy signature schemes and various extensions have been considered. As an example, the notion of threshold proxy signature or proxy multi-signature was introduced as a group-oriented variant. In this paper, we show that the threshold proxy signature scheme proposed by Hsu and Wu, and the proxy multi-signature schemes independently proposed by Chen et al. and Hsu et al. are all insecure against the malicious original singer(s). Our result provides a simple example that the way to put the secret parts together should be carefully considered.
1384536
Mining association rules with adjustable accuracy
In this paper, we devise efficient algorithms for mining association rules with adjustable accuracy. It is noted that several applications require mining the transaction data to capture the customer behavior frequently. In those applications, the efficiency of data mining could be a more important faktor t.han the requirement for complete accuracy of the mining results. Allowing imprecise results can significantly improve the data mining efficiency. In this paper, two methods for mining association rules with adjustable accuracy are developed. By dealing with the concept of sampling, both methods obtain some essential knowledge from a sampled subset first, and in light of that knowledge, perform efficient association rule mining on the entire database. A technique of relaxing the support factor based on the sampling size is devised to achieve the desired level of accuracy. These two methods differ from each other in their ways of utilizing the sampled data. Performance of these two methods is comparatively analyzed. As shown by our experimental results, the relaxation factor, as well as the sample size, can be properly adjusted so as to improve the result accuracy while minimizing the corresponding execution time, thereby allowing us to effectively achieve a design trade-off between accuracy and efficiency with two control parameters. It is shown that with the advantage of controlled sampling, the proposed methods are very flexible and efficient, and can in general lead to results of a very high degree of accuracy.
8794763
Video Retrieval of Human Interactions Using Model-Based Motion Tracking and Multi-layer Finite State Automata
Recognition of human interactions in a video is useful for video annotation, automated surveillance, and content-based video retrieval. This paper presents a model-based approach to motion tracking and recognition of human interactions using multi-layer finite state automata (FA). The system is used for widely-available, static-background monocular surveillance videos. A three-dimensional human body model is built using a sphere and cylinders and is projected on a two-dimensional image plane to fit the foreground image silhouette. We convert the human motion tracking problem into a parameter optimization problem without the need to compute inverse kinematics. A cost functional is used to estimate the degree of the overlap between the foreground input image silhouette and a projected three-dimensional body model silhouette. Motion data obtained from the tracker is analyzed in terms of feet, torso, and hands by a behavior recognition system. The recognition model represents human behavior as a sequence of states that register the configuration of individual body parts in space and time. In order to overcome the exponential growth of the number of states that usually occurs in single-level FA, we propose a multi-layer FA that abstracts states and events from motion data at multiple levels: low-level FA analyzes body parts only, and high-level FA analyzes the human interaction. Motion tracking results from video sequences are presented. Our recognition framework successfully recognizes various human interactions such as approaching, departing, pushing, pointing, and handshaking.
16088118
An effective hash-based algorithm for mining association rules
In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear in a sufficient number of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets first and then, identifying, within this candidate set, those itemsets that meet the large itemset requirement. Generally this is done iteratively for each large k-itemset in increasing order of k where a large k-itemset is a large itemset with k items. To determine large itemsets from a huge number of candidate large itemsets in early iterations is usually the dominating factor for the overall data mining performance. To address this issue, we propose an effective hash-based algorithm for the candidate set generation. Explicitly, the number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly. Extensive simulation study is conducted to evaluate performance of the proposed algorithm.
28122333
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
We examine the issue of mining association rules among items in a large database of sales transactions. Mining association rules means that, given a database of sales transactions, to discover all associations among items such that the presence of some items in a transaction will imply the presence of other items in the same transaction. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items that appear in a sufficient number of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets first, and then, identifying, within this candidate set, these itemsets that meet the large itemset requirement. Generally, this is done iteratively for each large k-itemset in increasing order of k, where a large k-itemset is a large itemset with k items. To determine large itemsets from a huge number of candidate sets in early iterations is usually the dominating factor for the overall data mining performance. To address this issue, we develop an effective algorithm for the candidate set generation. It is a hash-based algorithm and is especially effective for the generation of a candidate set for large 2-itemsets. Explicitly, the number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly. The advantage of the proposed algorithm also provides us the opportunity of reducing the amount of disk I/O required. An extensive simulation study is conducted to evaluate performance of the proposed algorithm.
16135620
Constructing fair-exchange protocols for E-commerce via distributed computation of RSA signatures
Applications such as e-commerce payment protocols, electronic contract signing, and certified e-mail delivery require that fair exchange be assured. A fair-exchange protocol allows two parties to exchange items in a fair way so that either each party gets the other's item, or neither party does. We describe a novel method of constructing very efficient fair-exchange protocols by distributing the computation of RSA signatures. Specifically, we employ multisignatures based on the RSA-signature scheme. To date, the vast majority of fair-exchange protocols require the use of zero-knowledge proofs, which is the most computationally intensive part of the exchange protocol. Using the intrinsic features of our multisignature model, we construct protocols that require no zero-knowledge proofs in the exchange protocol. Use of zero-knowledge proofs is needed only in the protocol setup phase--this is a one-time cost. Furthermore, our scheme uses multisignatures that are compatible with the underlying standard (single-signer) signature scheme, which makes it possible to readily integrate the fair-exchange feature with existing e-commerce systems.