15965432
Efficient key distribution schemes for secure media delivery in pay-TV systems
To provide secure media delivery in pay-TV systems, a large number of messages are exchanged for key updates in the conventional key distribution schemes. This is inefficient and costly when the client side (set-top box) uses a smart card with limited computing power. In this paper, we present three key distribution schemes for channel protection and secure media delivery in pay-TV systems. With the proposed schemes, encryption keys of the subscribed programs can be efficiently and securely distributed to the authorized subscribers. Only one message is needed to renew key in the key distribution schemes for subscription channel protection. In addition, we use simpler computation functions, including one-way hash function and exclusive-OR operation, for key updates to reduce the computation cost. With our key distribution schemes, only authorized subscribers can watch the subscribed programs correctly. Unauthorized subscribers have no information to retrieve the correct programs over the networks. Thus, service providers can charge their subscribers according to their subscriptions, and the illegal access of the media and video programs from networks can be prevented, based on the proposed schemes.
21281863
Practical key distribution schemes for channel protection
The paper presents three key distribution schemes for channel protection. With the proposed schemes, encryption keys of the ordered programs can be distributed to the authorized subscribers efficiently and securely. In these schemes, for key updates, at most two messages are transmitted and simpler computation functions, including one-way hash function and XOR operation, are used to reduce the computation cost compared to existing solutions. With our key distribution schemes, only the authorized subscribers can decrypt the ordered programs. Thus, the service provider in a Pay-TV system can charge his subscribers according to their subscriptions and the intelligent property rights of TV programs can be protected by the proposed schemes.
15471957
A Generic Electronic Payment Model Supporting Multiple Merchant Transactions
This paper presents a generic electronic payment model which is intended for a multi-merchant transaction, in which more than one merchant (the retailer, multiple resellers and content providers) are involved in the distribution of electronic contents. With our payment model, every involved merchant will be notified to verify the license information of a content on the settlement day before the customer actually pays for his purchase. Thus, piracy can be discouraged, intellectual property rights of electronic contents can be protected. Each of the involved merchants can obtain his own deserved share of the customer's payment in accordance with their union agreement on this content. As examples to demonstrate the applications of our generic payment model, two well-known payment systems are enhanced herein, based on our payment model.
5254746
Low-power techniques for network security processors
In this paper, we present several techniques for low-power design, including a descriptor-based low-power scheduling algorithm, design of dynamic voltage generator, and dual threshold voltage assignments, for network security processors. The experiments show that the proposed methods and designs provide the opportunity for network security processors to achieve the goals of both high performance and low power.
16023484
Asymptotically convergent modified recursive least-squares with data-dependent updating and forgetting factor
Continual updating of estimates required by most recursive estimation schemes often involves redundant usage of information and may result in system instabilities in the presence of bounded output disturbances. This paper investigates an algorithm which has the capability of eliminating these difficulties. Based on a set theoretic assumption, the algorithm yields modified least-squares estimates with a forgetting factor. It updates the estimates selectively depending on whether the observed data contain sufficient information. The information evaluation required at each step involves very simple computations. In addition, the parameter estimates are shown to converge asymptotically to a region around the true parameter at an exponential rate.
10970909
MAPGrid: A New Architecture for Empowering Mobile Data Placement in Grid Environments
The rising popularity of mobile applications and devices has brought about an enhanced interest in infrastructure support for mobile computing. Our work focuses on the development of a mobile grid infrastructure called MAPGrid (mobile applications on Grids), where grid resources are exploited as proxies to enable advanced mobile applications. However, intermittent availability of grid resources presents challenges for data-intensive mobile applications. In this paper, we propose novel methodologies for placing mobile data on grid proxies. We introduce a notion of two-tier architecture for MAPGrid, where the upper tier captures grid related features and the lower tier represents features associated with mobile environments. We further develop an intelligent mobile data placement mechanism that effectively balances tradeoffs between replication cost and data access cost by leveraging knowledge of grid availability and mobile data request patterns. Through extensive experimentation, we illustrate the superiority of the proposed techniques over several popular data placement strategies.
1232229
Active Mining of Data Streams
Most previously proposed mining methods on data streams make an unrealistic assumption that “labelled” data stream is readily available and can be mined at anytime. However, in most real-world problems, labelled data streams are rarely immediately available. Due to this reason, models are refreshed periodically, that is usually synchronized with data availability schedule. There are several undesirable consequences of this “passive periodic refresh”. In this paper, we propose a new concept of demand-driven active data mining. It estimates the error of the model on the new data stream without knowing the true class labels. When significantly higher error is suspected, it investigates the true class labels of a selected number of examples in the most recent data stream to verify the suspected higher error. 1 State-of-the-art Stream Mining State-of-the-art work on mining data streams concentrates on capturing time-evolving trends and patterns with “labeled” data. However, one important aspect that is often ignored or unrealistically assumed is the availability of “class labels” of data streams. Most algorithms make an implicit and impractical assumption that labeled data is readily available. Most works focus on how to detect the change in patterns and how to update the model to reflect such changes when there are “labelled” instances to be learned. However, for many applications, the class labels are not “immediately” available unless dedicated efforts and substantial costs are spent to investigate these labels right away. If the true class labels were readily available, data mining models would not be very useful we might just wait. In credit card fraud detection, we usually do not know if a particular transaction is a fraud until at least one month later after the account holder receives and reviews the monthly statement. Due to these facts, most current applications obtain class labels and update existing models in preset frequency, usually synchronized with data refresh. The effectiveness of the passive mode is dictated by some “statuary and static constraints”, yet not by the “demand” for a better model with a lower loss. Such a passive mode to mine data streams results in a number of potential undesirable consequences that contradict the notions of “streaming” and “continuous”. First, it may incur possibly higher loss due to neglected pattern drifts. If either the concept or data distribution drifts rapidly at an un-forecasted rate that statuary constraints do not catch up, the models are likely to be out-of-date on the data stream and important business opportunities might be missed. Second, it may have unnecessary model refresh. If there is neither conceptual nor distributional change, periodic passive model refresh and re-validation is a waste of resources. 1.1 Demand-driven Active Mining of Data Streams We are proposing a demand-driven active stream data mining process that solves the problems of passive stream data mining. As a summary, our particular implementation of active stream data mining has three simple steps: 1. Detect potential changes of data streams “on the fly” when the existing model classifies continuous data streams. The detection process does not use or know any true labels of the stream. One of the change detection methods is a “guess” of the actual loss or error rate of the model on the new data stream. 2. If the guessed loss or error rate of the model in step 1 is much higher than an application-specific tolerable maximum, we choose a small number of data records in the new data stream to investigate their true class labels. With these true class labels, we statistically estimate the true loss of the model. 3. If the statistically estimated loss in step 2 is verified to be higher than the tolerable maximum, we reconstruct the old model by using the same true class labels sampled in the previous step. In this paper, we concentrate on the first two steps. Our particular implementation extends on classification trees.
21184559
Region-based image retrieval
This paper presents a region-based method which uses multiple regions as the key to retrieve images. The proposed method represents the semantics or concepts embedded in the input images with three ingredients. One is a set of regions with weighted importance; the second is the corresponding feature distributions of the regions and the last is the spatial relationships between these regions. The importance of each segmented region in the input example images can be automatically and efficiently determined through a formulated linear system. In addition, a novel method for matching the spatial relationship between regions is also presented to capture the structural semantics of the content of images. By combining the feature distributions and the spatial relationships of regions with appropriate weights, the experimental results show that the retrieval results are much more accurate than other methods which utilize low-level features, such as color, texture, shape, and so on.