31934804
Digital Image Authentication Based on Turbo Codes
Image ownership authentication is an important part of copyright protection, and digital watermark can be used to implement this task. In this paper, we propose a new image authentication plan concentrating on its security performance. Secret information used as the copyright owner’s signature is first turbo coded, encrypted, scaled and then processed in wavelet domain. The original image is also needed in signature extraction. Simulation results are finally given to draw our conclusions.
41230139
Comments on an access control model in semantic grid
In this note, we show that there are some significant design weaknesses in the access control model that proposed to address the convergence and consistence problems in semantic grid by Bu and Xu in a recent paper.
44251234
Efficient and Proactive Threshold Signcryption
To make the system more secure and robust, threshold schemes are proposed to avoid single point failure. At the same time, there are more and more applications which utilize the two basic blocks encryption and digital signature to secure message delivery (such as SSL, SSH). Combining the three tools organically leads to an interesting security tool termed as threshold signcryption which can be used in distributed systems especially the mobile networks. In this paper, we present an efficient threshold signcryption scheme. The scheme is designed for an asynchronous network model which may better present practical distributed systems, especially Internet or mobile ad hoc networks. In order to resist mobile attacks, we add proactive property to our scheme. To the best of our knowledge, the proposed scheme is the first threshold signcryption scheme which is noninteractive, proactive and provably secure and works on asynchronous network models.
39321946
Attacks on the (enhanced) Yang-Shieh authentication
The Yang-Shieh authentication is a time-stamp based password authentication scheme that uses smart cards [1]. In [2,3], various attacks on this scheme are described. However, an enhancement of the scheme is proposed in [3] and enables the scheme to resist these existing attacks. In this paper, we show two new attack that can break the enhanced scheme. We further point out that the fundamental computational assumption of the Yang-Shieh authentication scheme is incorrect.
12064638
SDD: Secure Distributed Diffusion Protocol for Sensor Networks
A sensor network is a collection of tiny sensor nodes, which consists of sensing, data processing and communicating components. Directed Diffusion is an important data-centric routing protocol of sensor networks. In this paper we present the design of a new secure Directed Diffusion protocol (SDD), which provides a secure extension for the Directed Diffusion protocol. We mainly focus on secure routing and give a simple scheme to securely diffuse data. We have not considered with the in-network aggregation as a goal. In order to support the use of SDD for sensor nodes with extremely limited CPU processing capability, we use an efficient one-way chain and do not use asymmetric cryptographic operations in this protocol. Our security analyses show that SDD is robust against any active attackers or compromised nodes in the network.
875545
Multiplex Encryption: A Practical Approach to Encrypting Multi-recipient Emails
Efficiently protecting the privacy of multi-recipient emails is not as trivial as it seems. The approach proposed by S/MIME is to concatenate all ciphertexts. However, it suffers from poor scalability due to its linear computation and communication cost. In this paper, we propose a new practical and secure scheme, called multiplex encryption. By combining the ideas of identity-based mediated RSA and re-encryption mix network, our framework only requires constant computation and communication cost to encrypt a multi-recipient email.
16909772
On the use of nearest feature line for speaker identification
As a new pattern classification method, nearest feature line (NFL) provides an effective way to tackle the sort of pattern recognition problems where only limited data are available for training. In this paper, we explore the use of NFL for speaker identification in terms of limited data and examine how the NFL performs in such a vexing problem of various mismatches between training and test. In order to speed up NFL in decision-making, we propose an alternative method for similarity measure. We have applied the improved NFL to speaker identification of different operating modes. Its text-dependent performance is better than the dynamic time warping (DTW) on the Ti46 corpus, while its computational load is much lower than that of DTW. Moreover, we propose an utterance partitioning strategy used in the NFL for better performance. For the text-independent mode, we employ the NFL to be a new similarity measure in vector quantization (VQ), which causes the VQ to perform better on the KING corpus. Some computational issues on the NFL are also discussed in this paper.
34883067
Personalized News Reading via Hybrid Learning
In this paper, we present a personalized news reading prototype where latest news articles published by various on-line news providers are automatically collected, categorized and ranked in light of a user’s habits or interests. Moreover, our system can adapt itself towards a better performance. In order to develop such an adaptive system, we proposed a hybrid learning strategy; supervised learning is used to create an initial system configuration based on user’s feedbacks during registration, while an unsupervised learning scheme gradually updates the configuration by tracing the user’s behaviors as the system is being used. Simulation results demonstrate satisfactory performance.
52833236
Boosting Input/Output Hidden Markov Models for Sequence Classification
Input/output hidden Markov model (IOHMM) has turned out to be effective in sequential data processing via supervised learning. However, there are several difficulties, e.g. model selection, unexpected local optima and high computational complexity, which hinder an IOHMM from yielding the satisfactory performance in sequence classification. Unlike previous efforts, this paper presents an ensemble learning approach to tackle the aforementioned problems of the IOHMM. As a result, simple IOHMMs of different topological structures are used as base learners in our boosting algorithm and thus an ensemble of simple IOHMMs tend to tackle a complicated sequence classification problem without the need of explicit model selection. Simulation results in text-dependent speaker identification demonstrate the effectiveness of boosted IOHMMs for sequence classification.
17604450
A Practical Aspect Framework for Enforcing Fine-Grained Access Control in Web Applications
Access control is a system-wide concern that has both a generic nature and an application dependent characteristic. It is generic as many functions must be protected with restricted access, yet the rule to grant a request is highly dependent on the application state. Hence it is common to see the code for implementing access control scattered over the system and tangled with the functional code, making the system difficult to maintain. This paper addresses this issue for Web applications by presenting a practical access control framework based on aspect-oriented programming (AOP). Our approach accommodates a wide range of access control requirements of different granularity. AOP supports the modular implementation of access control while still enables the code to get a hold of the application state. Moreover, framework technology offers a balanced view between reuse and customization. As a result, our framework is able to enforce fine-grained access control for Web applications in a highly adaptable manner.
16405002
The fat-stack and universal routing in interconnection networks
This paper shows that a novel network called the fat-stack is universally efficient and is suitable for use as an interconnection network in parallel computers. A requirement for the fat-stack to be universal is that link capacities double up the levels of the network. The fat-stack resembles the fat-tree and the fat-pyramid in hardware structure, but it has unique strengths. It is a construct of an atomic subnetwork unit consisting of one ring and one or more upward links to an upper subnetwork. This simple structure entails easy wirability. The network also uses fewer wires. More importantly, it has the capability to scale up to represent a large-scale distributed network. We developed efficient routing algorithms specific to the fat-stack. Our universality proof shows that a fat-stack variant with increased links and of area @Q(A) can simulate any competing network of area A with O(logA) overhead independently of wire delay. The universality result implies that the augmented fat-stack of a given size is nearly the best routing network of that size. The augmented fat-stack is the minimal universal network for an O(logA) overhead in terms of hardware usage. Actual simulations show that the performance of the augmented fat-stack approaches that of the fat-pyramid and is far higher than that of the fat-tree.