199790
Secure group communication using robust contributory key agreement
Contributory group key agreement protocols generate group keys based on contributions of all group members. Particularly appropriate for relatively small collaborative peer groups, these protocols are resilient to many types of attacks. Unlike most group key distribution protocols, contributory group key agreement protocols offer strong security properties such as key independence and perfect forward secrecy. We present the first robust contributory key agreement protocol resilient to any sequence of group changes. The protocol, based on the Group Diffie-Hellman contributory key agreement, uses the services of a group communication system supporting virtual synchrony semantics. We prove that it provides both virtual synchrony and the security properties of Group Diffie-Hellman, in the presence of any sequence of (potentially cascading) node failures, recoveries, network partitions, and heals. We implemented a secure group communication service, Secure Spread, based on our robust key agreement protocol and Spread group communication system. To illustrate its practicality, we compare the costs of establishing a secure group with the proposed protocol and a protocol based on centralized group key management, adapted to offer equivalent security properties.
7213506
Batch Verifications with ID-Based Signatures
An identity (ID)-based signature scheme allows any pair of users to verify each other’s signatures without exchanging public key certificates. With the advent of Bilinear maps, several ID-based signatures based on the discrete logarithm problem have been proposed. While these signatures have an advantage in the fact that the system secret can be shared by several parties using a threshold scheme (thereby overcoming the security problem of RSA-based ID-based signature schemes), they all share the same efficiency disadvantage. To overcome this, some schemes have focused on finding ways to verify multiple signatures at the same time (i.e. the batch verification problem). While they had some success in improving efficiency of verification, each had a slightly diversified definition of batch verification. In this paper, we propose a taxonomy of batch verification against which we analyze security of well-known ID-based signature schemes. We also propose a new ID-based signature scheme that allows for all types of multiple signature batch verification, and prove its security in random oracle model.
6315809
On the performance of group key agreement protocols
Group key agreement is a fundamental building block for secure peer group communication systems. Several group key management techniques were proposed in the last decade, all assuming the existence of an underlying group communication infrastructure to provide reliable and ordered message delivery as well as group membership information. Despite analysis, implementation, and deployment of some of these techniques, the actual costs associated with group key management have been poorly understood so far. This resulted in an undesirable tendency: on the one hand, adopting suboptimal security for reliable group communication, while, on the other hand, constructing excessively costly group key management protocols.This paper presents a thorough performance evaluation of five notable distributed key management techniques (for collaborative peer groups) integrated with a reliable group communication system. An in-depth comparison and analysis of the five techniques is presented based on experimental results obtained in actual local- and wide-area networks. The extensive performance measurement experiments conducted for all methods offer insights into their scalability and practicality. Furthermore, our analysis of the experimental results highlights several observations that are not obvious from the theoretical analysis.
6168983
Privacy Preserving Nearest Neighbor Search
Data mining is frequently obstructed by privacy concerns. In many cases data is distributed, and bringing the data together in one place for analysis is not possible due to privacy laws (e.g. HIPAA) or policies. Privacy preserving data mining techniques have been developed to address this issue by providing mechanisms to mine the data while giving certain privacy guarantees. In this work we address the issue of privacy preserving nearest neighbor search, which forms the kernel of many data mining applications. To this end, we present a novel algorithm based on secure multiparty computation primitives to compute the nearest neighbors of records in horizontally distributed data. We show how this algorithm can be used in three important data mining algorithms, namely LOF outlier detection, SNN clustering, and kNN classification
14346631
Remote Software-Based Attestation for Wireless Sensors
Wireless sensor networks are envisioned to be deployed in mission-critical applications. Detecting a compromised sensor, whose memory contents have been tampered, is crucial in these settings, as the attacker can reprogram the sensor to act on his behalf. In the case of sensors, the task of verifying the integrity of memory contents is difficult as physical access to the sensors is often infeasible. In this paper, we propose a software-based approach to verify the integrity of the memory contents of the sensors over the network without requiring physical contact with the sensor. We describe the building blocks that can be used to build a program for attestation purposes, and build our attestation program based on these primitives. The success of our approach is not dependent on accurate measurements of the execution time of the attestation program. Further, we do not require any additional hardware support for performing remote attestation. Our attestation procedure is designed to detect even small memory changes and is designed to be resistant against modifications by the attacker.
8456310
Detecting Phantom Nodes in Wireless Sensor Networks
In an adversarial environment, various kinds of security attacks become possible if malicious nodes could claim fake locations that are different from where they are physically located. In this paper, we propose a secure localization mechanism that detects the existence of these nodes, termed as phantom nodes, without relying on any trusted entities, an approach significantly different from the existing ones. The proposed mechanism enjoys a set of nice features. First, it does not have any central point of attack. All nodes play the role of verifier, by generating local map, i.e. a view constructed based on ranging information from its neighbors. Second, this distributed and localized construction results in quite strong results: even when the number of phantom nodes is greater than that of honest nodes, we can Alter out most phantom nodes. Our analysis and simulations under realistic noisy settings demonstrate our scheme is effective in the presence of a large number of phantom nodes.
28723686
Implementation of ECC/ECDSA cryptography algorithms based on Java card
This paper describes the implementations and test results of elliptic curve cryptography (ECC) and elliptic curve digital signature algorithm (ECDSA) algorithms based on Java card. A 163-bit ECC guarantees as secure as the 1024-bit Rivest-Shamir-Adleman (RSA) public key algorithm, which has been frequently used until now. According to our test results, the 163-bit ECC processing time is about five times faster compared with the 1024-bit RSA, and the amount of resource usages of ECC is smaller than RSA. Therefore, ECC is more appropriate for use on secure devices such as smart cards and wireless devices with constrained computational power consumption and memory resources.
8330560
Shape Comparison of the Hippocampus Using a Multiresolution Representation and ICP Normalization
It is known that deformation of the hippocampus shape is involved with several neurological diseases. In this paper, we propose a hybrid shape representation scheme, which consists of multiresolution skeletons, voxels and meshes for the shape analysis of the hippocampus. Initially, a hippocampal surface model is reconstructed from MRI and then it is placed into a canonical coordinate system, where the position, orientation and scaling are normalized. From the voxel representation of the hippocampus, multiresolution skeletons are extracted and Iterative Closest Point normalization is carried out. Then the shape similarity of two hippocampal models is computed with a hierarchical fashion. In addition, we have implemented a neural network based classifier to discriminate whether a hippocampal model is normal or not. Results indicate that the proposed hybrid representation and the skeleton-based normalization using ICP are very effective in 3D shape analysis of the hippocampus.
344302
New topologies for genetic search space
We propose three distance measures for genetic search space. One is a distance measure in the population space that is useful for understanding the working mechanism of genetic algorithms. Another is a distance measure in the solution space for K-grouping problems. This can be used for normalization in crossover. The third is a level distance measure for genetic algorithms, which is useful for measuring problem difficulty with respect to genetic algorithms. We show that the proposed measures are metrics and the measures are efficiently computed.
36946020
Face Representation Method Using Pixel-to-Vertex Map (PVM) for 3D Model Based Face Recognition
3D model based approach for face recognition has been spotlighted as a robust solution under variant conditions of pose and illumination. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time. In this paper, we propose a novel 3D face representation algorithm to reduce the number of vertices and optimize its computation time. Finally, we evaluate the performance of proposed algorithm with the Korean face database collected using a stereo-camera based 3D face capturing device.
7461874
Face and Facial Expression Recognition with an Embedded System for Human-Robot Interaction
In this paper, we present an embedded system in which face recognition and facial expression recognition for Human-Robot Interaction are implemented. To detect face with a fast and reliable way, AdaBoost algorithm is used. Then, Principal Component Analysis is applied for recognizing the face. Gabor wavelets are combined with Enhanced Fisher Model for facial expression recognition. Performance of the facial expression recognition reaches to 93%. The embedded system runs on 150MHz and the processing speed is 0.6 frames / second. Experimental result demonstrates that face detection, recognition and facial expression can be implemented with an embedded system for the Human-Robot Interaction.