Efficient Single-pass Algorithm Research Articles

Spook: Sponge-Based Leakage-Resistant Authenticated Encryption with a Masked Tweakable Block Cipher

This paper defines Spook: a sponge-based authenticated encryption with associated data algorithm. It is primarily designed to provide security against side-channel attacks at a low energy cost. For this purpose, Spook is mixing a leakageresistant mode of operation with bitslice ciphers enabling efficient and low latency implementations. The leakage-resistant mode of operation leverages a re-keying function to prevent differential side-channel analysis, a duplex sponge construction to efficiently process the data, and a tag verification based on a Tweakable Block Cipher (TBC) providing strong data integrity guarantees in the presence of leakages. The underlying bitslice ciphers are optimized for the masking countermeasures against side-channel attacks. Spook is an efficient single-pass algorithm. It ensures state-of-the-art black box security with several prominent features: (i) nonce misuse-resilience, (ii) beyond-birthday security with respect to the TBC block size, and (iii) multiuser security at minimum cost with a public tweak. Besides the specifications and design rationale, we provide first software and hardware implementation results of (unprotected) Spook which confirm the limited overheads that the use of two primitives sharing internal components imply. We also show that the integrity of Spook with leakage, so far analyzed with unbounded leakages for the duplex sponge and a strongly protected TBC modeled as leak-free, can be proven with a much weaker unpredictability assumption for the TBC. We finally discuss external cryptanalysis results and tweaks to improve both the security margins and efficiency of Spook.

Mining High Utility Itemsets with Regular Occurrence

High utility itemset mining (HUIM) plays an important role in the data mining community and in a wide range of applications. For example, in retail business it is used for finding sets of sold products that give high profit, low cost, etc. These itemsets can help improve marketing strategies, make promotions/advertisements, etc. However, since HUIM only considers utility values of items/itemsets, it may not be sufficient to observe product-buying behavior of customers such as information related to purchases of sets of products having a high profit margin. To address this issue, the occurrence behavior of itemsets (in the term of regularity) simultaneously with their utility values was investigated. Then, the problem of mining high utility itemsets with regular occurrence (MHUIR) to find sets of co-occurrence items with high utility values and regular occurrence in a database was considered. An efficient single-pass algorithm, called MHUIRA, was introduced. A new modified utility-list structure, called NUL, was designed to efficiently maintain utility values and occurrence information and to increase the efficiency of computing the utility of itemsets. Experimental studies on real and synthetic datasets and complexity analyses are provided to show the efficiency of MHUIRA combined with NUL in terms of time and space usage for mining interesting itemsets based on regularity and utility constraints.

Mining top-k frequent-regular closed patterns

Frequent-regular pattern mining has attracted recently many works. Most of the approaches focus on discovering a complete set of patterns under the user-given support and regularity threshold constraints. This leads to several quantitative and qualitative drawbacks. First, it is often difficult to set appropriate support threshold. Second, algorithms produce a huge number of patterns, many of them being redundant. Third, most of the patterns are of very small size and it is arduous to extract interesting relationship among items. To reduce the number of patterns a common solution is to consider the desired number k of outputs and to mine the top-k patterns. In addition, this approach does not require to set a support threshold. To cope with redundancy and interestingness relationship among items, we suggest to focus on closed patterns and introduce a minimal length constraint. We thus propose to mine the top-k frequent-regular closed patterns with minimal length. An efficient single-pass algorithm, called TFRC-Mine, and a new compact bit-vector representation which allows to prune uninteresting candidate, are designed. Experiments show that the proposed algorithm is efficient to produce longer – non redundant – patterns, and that the new data representation is efficient for both computational time and memory usage.

A single-pass triclustering algorithm

As the popularity of the field of big data continues to rise, the problem of the development of efficient algorithms with low time complexity and that ability to be parallelized is more and more frequently posed. This work is aimed at the development of an efficient single-pass algorithm for the triclustering of binary data that is suitable for use in the field of big data. As a result, a single-pass serial online OAC triclustering algorithm (triclustering of object-attribute-condition) was obtained. This algorithm not only has a very low complexity in time and memory, but it also can be effectively parallelized.

An Efficient Single-Pass Algorithm for Mining Association Rules from Wireless Sensor Networks

With advances in technology, use of wireless sensor networks (WSNs) has widely increased in recent decades. In general, WSNs produce a large amount of data in the form of streams. Recently, data-mining techniques have received a great deal of attention for their utility in extracting knowledge from WSN data. Mining association rules on the sensor data provides useful information for different applications. Even though there have been some efforts to address this issue in WSNs, they are not suitable when the user might not have enough opportunity to scan the database multiple times, which is highly common in the WSN environment. Therefore, in this paper we propose a new tree-based data structure called sensor pattern tree (SP-tree) to generate the set of all association rules from WSN data with one scan over the sensor database. The SP-tree is constructed in frequency-descending order, which facilitates an efficient mining using the frequent pattern (FP)-growth-based mining technique. Our experimental results show that SP-tree outperforms related algorithms in finding association rules from WSN data.

Mining top- k Hot Melody Structures over online music query streams

Online mining of frequent patterns from music data is one of the most important research issues of multimedia data mining. Most previous studies require the specification of a min_ support threshold and aim at mining a complete set of frequent patterns satisfying min_ support. However, in practice, it is difficult for users to provide an appropriate value of min_ support threshold. In this paper, we propose a new problem of multimedia data mining: online mining of top- k melody structures of length no less than min_ l, where k is the desired number of hot melody structures to be mined and min_ l is the minimal length of each melody structure. An efficient single-pass algorithm, called top- k-HMS (top- k Hot Melody Structures), is developed for mining such melody structures without min_ support. In the framework of top- k-HMS algorithm, a new summary data structure, called TKM-list (top- k melody list) is developed to maintain the essential information about the top- k hot melody structures from the current melody sequence streams. Experimental studies show that the proposed top- k-HMS algorithm is an efficient one-pass method for mining the set of top- k Hot Melody Structures over a continuous stream of melody sequences.

Circuit structure relations to redundancy and delay

The existence of redundant stuck-faults in a logic circuit is potentially detrimental to high-speed operation, especially when there are false paths that are longer than the circuit delay. Keutzer, Malik, and Saldanha (KMS) in IEEE transactions of Computer Aided Design, vol. 10, no. 4, p. 427, April 1991 have proved that redundancy is not necessary to reduce delay by presenting an algorithm that derives an equivalent irredundant circuit from a given redundant circuit, with no increase in delay. The KMS algorithm consists of an iterative loop of timing analysis, gate duplications, and redundancy removal to successively eliminate long false paths. In this paper we resolve the main bottlenecks of the KMS algorithm by providing an efficient single-pass algorithm to simultaneously remove all long false paths from a given circuit. We achieve this by relating a circuit structure property based on path lengths to the testability (redundancy) and delay. The application of this algorithm to a variety of related logic synthesis problems is described.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>