Quite recently [IEEE Commu. Letters, Vol.14, No.1, 2010], Choi et al. proposed a handover authentication scheme using credentials based on chameleon hashing, claiming to provide several security features including Perfect Forward/Backward Secrecy (PFS/PBS). This paper examines the security of the scheme and shows that the scheme still fails to achieve PFS/PBS unlike their claims.

Despite their usability advantages over traditional authentication systems, biometrics-based authentication systems suffer from inherent privacy violation and non-revocability issues. In order to address these issues, the concept of cancelable biometrics was introduced as a means of generating multiple, revocable, and noninvertible identities from true biometric templates. Apart from BioHashing, which is a two-factor cancelable biometrics technique based on mixing a set of tokenized user-specific random numbers with biometric features, cancelable biometrics techniques usually cannot preserve the recognition accuracy achieved using the unprotected biometric systems. However, as the employed token can be lost, shared, or stolen, BioHashing suffers from the same issues associated with token-based authentication systems. In this paper, a reliable tokenless cancelable biometrics scheme, referred to as BioEncoding, for protecting IrisCodes is presented. Unlike BioHashing, BioEncoding can be used as a one-factor authentication scheme that relies only on sole IrisCodes. A unique noninvertible compact bit-string, referred to as BioCode, is randomly derived from a true IrisCode. Rather than the true IrisCode, the derived BioCode can be used efficiently to verify the user identity without degrading the recognition accuracy obtained using original IrisCodes. Additionally, BioEncoding satisfies all the requirements of the cancelable biometrics construct. The performance of BioEncoding is compared with the performance of BioHashing in the stolen-token scenario and the experimental results show the superiority of the proposed method over BioHashing-based techniques.

The traditional cryptographic hash functions are sensitive to even one-bit difference of the input message. While multimedia data always undergo compression or other signal processing operations, which lead to the unsuitability of multimedia authentication using cryptographic hash. The image hashing has emerged recently which captures visual essentials for robust image authentication. In this paper, we give a comprehensive survey of image hashing. We present an overview of various image hashing schemes and discuss their advantages and limitations in terms of security, robustness, and discrimination under different types of operations on the image.

This paper describes a novel algorithm for approximate nearest neighbor searching. For solving this problem especially in high dimensional spaces, one of the best-known algorithm is Locality-Sensitive Hashing (LSH). This paper presents a variant of the LSH algorithm that outperforms previously proposed methods when the dataset consists of vectors normalized to unit length, which is often the case in pattern recognition. The LSH scheme is based on a family of hash functions that preserves the locality of points. This paper points out that for our special case problem we can design efficient hash functions that map a point on the hypersphere into the closest vertex of the randomly rotated regular polytope. The computational analysis confirmed that the proposed method could improve the exponent ρ, the main indicator of the performance of the LSH algorithm. The practical experiments also supported the efficiency of our algorithm both in time and in space.

A multiple stream join is one of the most important but high cost operations in ubiquitous streaming services. In this paper, we propose a newly improved and practical algorithm for joining multiple streams called AMJoin, which improves the multiple join performance by guaranteeing the detection of join failures in constant time. To achieve this goal, we first design a new data structure called BiHT (Bit-vector Hash Table) and present the overall behavior of AMJoin in detail. In addition, we show various experimental results and their analyses for clarifying its efficiency and practicability.

We propose an efficient dynamic hash index structure suitable for a NAND flash memory environment. Since write operations incur significant overhead in NAND flash memory, our design of index structure focuses on minimizing the number of write operations for hash index updates. Through a set of extensive experiments, we show the effectiveness of the proposed hash index structure in a NAND flash memory environment.

Several hardware-based pattern matching engines for network intrusion/prevention detection systems (NIDS/NIPSs) can achieve high throughput with less hardware resources. However, their flexibility to update new patterns is limited and still challenging. This paper describes a PAttern Matching Engine with Limited-time updAte (PAMELA) engine using a recently proposed hashing algorithm called Cuckoo Hashing. PAMELA features on-the-fly pattern updates without reconfiguration, more efficient hardware utilization, and higher performance compared with other works. First, we implement the improved parallel exact pattern matching with arbitrary length based on Cuckoo Hashing and linked-list technique. Second, while PAMELA is being updated with new attack patterns, both stack and FIFO are utilized to bound insertion time due to the drawback of Cuckoo Hashing and to avoid interruption of input data stream. Third, we extend the system for multi-character processing to achieve higher throughput. Our engine can accommodate the latest Snort rule-set, an open source NIDS/NIPS, and achieve the throughput up to 8.8 Gigabit per second while consuming the lowest amount of hardware. Compared to other approaches, ours is far more efficient than any other implemented on Xilinx FPGA architectures.

This paper investigates suitable indexing techniques to enable efficient content-based audio retrieval in large acoustic databases. To make an index-based retrieval mechanism applicable to audio content, we investigate the design of Locality Sensitive Hashing (LSH) and the partial sequence comparison. We propose a fast and efficient audio retrieval framework of query-by-content and develop an audio retrieval system. Based on this framework, four different audio retrieval schemes, LSH-Dynamic Programming (DP), LSH-Sparse DP (SDP), Exact Euclidian LSH (E2LSH)-DP, E2LSH-SDP, are introduced and evaluated in order to better understand the performance of audio retrieval algorithms. The experimental results indicate that compared with the traditional DP and the other three compititive schemes, E2LSH-SDP exhibits the best tradeoff in terms of the response time, retrieval accuracy and computation cost.

Geographic distributed hash table (DHT) protocols are considered to be efficient for P2P object sharing in mobile ad-hoc networks. These protocols assume that the set of pairs, called indexes, should be distributed among nodes according to the following hashing mapping rule: A key hashes into a geographic coordinate, and the corresponding index is stored at the node closest to the key's hash value. Therefore, when a node changes its position, some indexes have to be redistributed to other nodes in order to keep the hashing mapping rule consistent. The overhead of index redistribution may be high enough to impact the normal lookup operation if each node contains a large number of indexes. In this paper, we propose an efficient lookup protocol, called Double Indirect Access, that dispenses with index redistribution to improve lookup performance. The main idea is to determine the mapping from an index to a node not by the node's position, but by the node's static identifier that is obtained by hashing its MAC address into a geographic coordinate. However, a key lookup request will be routed to some node based on the key's hash value, resulting in failure of locating the index. In Double Indirect Access, the node to which a key lookup request has been routed is named as an indirection server, and it is responsible for relaying the lookup request to the node storing the corresponding index. In order for the indirection server to find out the correct destination node for the lookup request, it maintains a list of nodes' static identifiers whose values (i.e., geographic coordinates) are close to the location of the indirection server. Simulation results show that, when the average number of objects per node is more than 256, our approach is able to reduce the number of packet transmissions by about a half compared to the conventional geographical DHT protocol. It is also shown that, even when the average number of objects per node is about 9-16, the overhead of our approach is comparable with the conventional protocol.

The chaotic property of a new open addressing hash function, called exponential hashing, is presented. Our analysis indicates the connection between ergodic theory and hashing. Based on that, concepts from ergodic theory are applied to predict the performance of exponential hashing. Experimental results are presented to verify our theoretic analysis and the prediction.

The linear hashing is a well-known dynamic hashing algorithm designed for internal main memory as well as external secondary memory. Traditional analysis of this search algorithm has been proposed under the assumption that all keys are uniformly accessed. In this paper, we present a discrete analysis of the average search cost of the linear dynamic hashing algorithm for internal main memory in consideration of the frequency of access on each key. In the proposed discrete analysis, the number of probes itself is regarded as a random variable and its probability distribution is derived concretely. Furthermore, the evaluate formula derived from the proposed analysis can exactly evaluate the average search cost in conformity with any probability distribution of the frequency of access. The proposed analysis is compared to the traditional one provided that the frequency of access on each key is uniform, and the differences are discussed.

In a conventional file system, the directory tree is traversed to find the inode number of a file. The inode lookup performance degrades as the size of the directory tree increases. In this letter, a new directory scheme, called direct hashing directory, is proposed. The inode number of a file is the cyclic redundancy code of the file's absolute path name such that the inode number can be computed directly. The average number of disk accesses for inode lookup is 1.08, which is order of magnitude faster than the conventional directory schemes such as hashing, B tree, and sequential directory.

Spiral hashing is a well known dynamic hashing algorithm. Traditional analysis of this search algorithm has been proposed under the assumption that all keys are uniformly accessed. In this paper, we present a discrete analysis of the average search cost in consideration of the frequency of access on each key for this spiral hashing algorithm. In the proposed discrete analysis, the number of probes itself is regarded as a random variable and its probability distribution is derived concretely. The evaluate formulae derived from the proposed analysis can exactly evaluate the average and variance of the search cost in conformity with any probability distribution of the frequency of access.

A novel approach was proposed to recognize the non-rigid 3D objects from their corresponding 2D images by combining the benefits of the principal component analysis and the geometric hashing. For all of the object models to be recognized, we calculated the statistical point features of the training shapes using principal component analysis. The results of the analysis were a vector of eigenvalues and a matrix of eigenvectors. We calculated invariants of the new shapes that undergone a similarity transformation. Then added these invariants and the label of the model to the model database. To recognize objects, we calculated the necessary invariants from an unknown image and used them as the indexing keys to retrieve any possible matches with the model features from the model database. We hypothesized the existence of an instance of the model in the scene if the model's features scored enough hits on the vote count. This approach allowed us to store the rigid and the non-rigid object models in a model database and utilized them to recognize an instance of model from an unknown image.

In this paper, we propose a descriptor as a shape signature and the projective refinement as a verification method for recognizing 2D curved objects with occlusions from their partial views. For an extracted curve segment, we compute a series of the geometric invariance of equally spaced five co-planar points on the curve. Thus the resulting descriptor is invariant only under rotation, translation, and scale, but sufficient similarity is preserved even under large distortions. It is more stable and robust since it does not need derivatives. We use this transformation-invariant descriptor to index a hash table. We show the efficiency of the method through experiments using seriously distorted images of 2-D curved objects with occlusions.

The storage utilizations of existing similar key search files based on B+-tree and extensible hashing were under 70% and should be improved. A similar key search file based on extensible hashing with partial expansion and that on linear hashing with partial expansion are proposed. Computer simulations on about 230 thousand English words show that the storage utilizations of the files with 32 expansive steps are about 97%.

The organization of the proposed file and algorithms for search and maintenance of the file are simpler than those of a similar key search file based on B+-tree. An experiment using 230,188 keys with length 1-16 shows the good performance of the file. The storage utilization of the file is about 68%.