1962595
Admission control in peer groups
Security in collaborative peer groups is an active research topic. Most previous work focused on key management without addressing an important pre-requisite: admission control, i.e., how to securely admit a new member. This paper represents an initial attempt to sketch out an admission control framework suitable for different flavors of peer groups and match them with appropriate cryptographic techniques and protocols. Open problems and directions for future work are identified and discussed.
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
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.
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.
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.
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.
37111410
Performance Comparison Between Backpropagation, Neuro-Fuzzy Network, and SVM
In this study, we compare the performance of well-known neural networks, namely, back-propagation (BP) algorithm, Neuro-Fuzzy network and Support Vector Machine (SVM) using the standard three database sets: Wisconsin breast cancer, Iris and wine data. Since such database have been useful for evaluating performance of a group of machine learning algorithms, a series of experiments have been carried out for three algorithms using the cross validation method. Results suggest that SVM outperforms the others and the Neuro-Fuzzy network is better than the BP algorithm for this data set.
36639292
Lock-Gain Based Graph Partitioning
We propose a new heuristic for the graph partitioning problem. Based on the traditional iterative improvement framework, the heuristic uses a new type of gain in selecting vertices to move between partitions. The new type of gain provides a good explanation for the performance difference of tie-breaking strategies in KL-based iterative improvement graph partitioning algorithms. The new heuristic performed excellently. Theoretical arguments supporting its efficacy are also provided. As the proposed heuristic is considered a good candidate for local optimization engines in metaheuristics, we combined it with a genetic algorithm as a sample case and obtained a surprising result that even the average results over 1,000 runs equalled the best known for most graphs.
27587473
Generalized cycle crossover for graph partitioning
We propose a new crossover that generalizes cycle crossover to permutations with repetitions and naturally suits partition problems. We tested it on graph partitioning problems obtaining excellent results.
14647313
Energy-efficient disk replacement and file placement techniques for mobile systems with hard disks
Mobile systems have usually used hard disks as the secondary storage devices because of their high capacity per cost and high I/O throughput. However, their high power consumption is the main limiting factor for extending their adoptions in mobile systems. In this paper, we propose enhanced file placement techniques for mobile platforms with multiple smaller disks (instead of a single large disk). We investigate that how many smaller disks are necessary to obtain energy saving while maintaining the required performance using both a simplified energy model and a realistic trace-based simulator under the proposed multiple disk configurations. We also propose energy-efficient file placement techniques, which aggregate files with common attributes the same set of disks. By skewing I/O operations, the proposed techniques achieve additional energy saving. Experimental results show that the proposed techniques can reduce the energy consumption by up to 43% when eight 1" disks are used instead of a single 2.5" disk with an acceptable increase in the average response time.
2299968
Multi-campaign Assignment Problem and Optimizing Lagrange Multipliers
Customer relationship management is crucial in acquiring and maintaining royal customers. To maximize revenue and customer satisfaction, companies try to provide personalized services for customers. A representative effort is one-to-one marketing. The fast development of Internet and mobile communication boosts up the market of one-to-one marketing. A personalized campaign targets the most attractive customers with respect to the subject of the campaign. So it is important to expect customer preferences for campaigns. Collaborative Filtering (CF) and various data mining techniques are used to expect customer preferences for campaigns. Especially, since CF is fast and simple, it is widely used for personalization in e-commerce. There have been a number of customer-preference estimation methods based on CF. As personalized campaigns are frequently performed, several campaigns often happen to run simultaneously. It is often the case that an attractive customer for a specific campaign tends to be attractive for other campaigns. If we perform separate campaigns without considering this problem, some customers may be bombarded by a considerable number of campaigns. We call this overlapped recommendation problem. The larger the number of recommendations for a customer, the lower the customer interest for campaigns. In the long run, the customer response for campaigns drops. It lowers the marketing efficiency as well as customer satisfaction. Unfortunately, traditional methods only focused on the effectiveness of a single campaign and did not consider the problem with respect to the overlapped recommendations. In this paper, we define the multi-campaign assignment problem (MCAP) considering the overlapped recommendation problem and propose a number of methods for the issue including a genetic approach. We also verify the effectiveness of the proposed methods with field data.