52804588
Cryptanalysis of the Full HAVAL with 4 and 5 Passes
HAVAL is a cryptographic hash function with variable digest size proposed by Zheng, Pieprzyk and Seberry in 1992. It has three variants, 3-, 4-, and 5-pass HAVAL. Previous results on HAVAL suggested only practical collision attacks for 3-pass HAVAL. In this paper, we present collision attacks for 4 and 5 pass HAVAL. For 4-pass HAVAL, we describe two practical attacks for finding 2-block collisions, one with 243 computations and the other with 2 36 computations. In addition, we show that collisions for 5-pass HAVAL can be found with about 2 123 computations, which is the first attack more efficient than the birthday attack.
1545116
Finding Collisions in the Full SHA-1
In this paper, we present new collision search attacks on the hash function SHA-1. We show that collisions of SHA-1 can be found with complexity less than 269 hash operations. This is the first attack on the full 80-step SHA-1 with complexity less than the 280 theoretical bound.
36451114
Sensor Network Configuration Under Physical Attacks
Sensor networks typically operate in hostile outdoor environments. In such environments, sensor networks are highly susceptible to physical attacks that can result in physical node destructions. In this paper, we study the impacts of physical attacks on sensor network configuration. Lifetime is an important metric during configuration for many sensor applications. While lifetime is constrained by limited energies and has been addressed before, prior results cannot be directly applied in the presence of physical attacks. In this paper, we define a practical lifetime problem in sensor networks under a representative physical attack model that we define. We develop an anlytical approach to derive the minimum number and deployment plan of sensors to meet lifetime requirement under physical attacks. We make several observations in this paper. One of our important observations is the high sensitivity of lifetime to physical attacks highlighting the significance of our study.
15578823
Exploring statistical correlations for image retrieval
Bridging the cognitive gap in image retrieval has been an active research direction in recent years, of which a key challenge is to get enough training data to learn the mapping functions from low-level feature spaces to high-level semantics. In this paper, image regions are classified into two types: key regions representing the main semantic contents and environmental regions representing the contexts. We attempt to leverage the correlations between types of regions to improve the performance of image retrieval. A Context Expansion approach is explored to take advantages of such correlations by expanding the key regions of the queries using highly correlated environmental regions according to an image thesaurus. The thesaurus serves as both a mapping function between image low-level features and concepts and a store of the statistical correlations between different concepts. It is constructed through a data-driven approach which uses Web data (images, their surrounding textual annotations) as training data source to learn the region concepts and to explore the statistical correlations. Experimental results on a database of 10,000 general-purpose images show the effectiveness of our proposed approach in both improving search precision (i.e. filter irrelevant images) and recall (i.e. retrieval relevant images whose context may be varied). Several major factors which have impact on the performance of our approach are also studied.
13026577
Auditing Interval-Based Inference
In this paper we study the feasibility of auditing interval-based inference. Sensitive information about individuals is said to be compromised if an accurate enoughin terval, called inference interval, is obtained into which the value of the sensitive information must fall. Compared with auditing exact inference that is traditionally studied, auditing interval-based inference is more complicated. Existing auditing methods such as audit expert do not apply to this case. Our result shows that it is intractable to audit interval-based inference for bounded integer values; while for bounded real values, the auditing problem is polynomial yet involves complicated computation of mathematical programming. To further examine the practicability of auditing interval-based inference, we classify various auditing methods into three categories: exact auditing, optimistic auditing, and pessimistic auditing. We analyze the trade-offs that can be achieved by these methods among various auditing objectives: inference security, database usability, and auditing complexity.
16978334
Evaluating a class of distance-mapping algorithms for data mining and clustering
A distance-mapping algorithm takes a set of objects and a distance metric and then maps those objects to a Euclidean or pseudoEuclidean space in such a way that the distances among objects are approximately preserved. Distance mapping algorithms are a useful tool for clustering and visualization in data intensive applications, because they replace expensive distance calculations by sum-of-square calculations. This can make clustering in large databases with expensive distance metrics practical. In this paper we present five distance-mapping algorithms and conduct experiments to compare their performance in data clustering applications. These include two algorithms called FastMap and MetricMap, and three hybrid heuristics that combine the two algorithms in different ways. Experimental results on both synthetic and RNA data show the superiority of the hybrid algorithms. The results imply that FastMap and MetricMap capture complementary information about distance metrics and therefore can be used together to great benefit. The net effect is that multi-day computations may be done in minutes.
15083897
Grouping web image search result
In this paper, we propose a Web image search result organizing method to facilitate user browsing. We formalize this problem as a salient image region pattern extraction problem. Given the images returned by Web search engine, we first segment the images into homogeneous regions and quantize the environmental regions into image codewords. The salient codeword "phrases" are then extracted and ranked based on a regression model learned from human labeled training data. According to the salient "phrases", images are assigned to different clusters, with the one nearest to the centroid as the entry for the corresponding cluster. Satisfying experimental results show the effectiveness of our proposed method.