Faster Computation of Self-pairings

Self-pairings have found interesting applications in cryptographic schemes. In this paper, we present a novel method for constructing a self-pairing on supersingular elliptic curves with even embedding degrees, which we call the Ateil pairing. This new pairing improves the efficiency of the self-pairing computation on supersingular curves over finite fields with large characteristics. Based on the $\eta_T$ pairing, we propose a generalization of the Ateil pairing, which we call the Ateil$_i$ pairing. The optimal Ateil$_i$ pairing which has the shortest Miller loop is faster than previously known self-pairings on supersingular elliptic curves over finite fields with small characteristics. We also present a new self-pairing based on the Weil pairing which is faster than the self-pairing based on the Tate pairing on ordinary elliptic curves with embedding degree $one$

Efficient algorithms for pairing-based cryptosystems

We describe fast new algorithms to implement recent cryptosystems based on the Tate pairing. In particular, our techniques improve pairing evaluation speed by a factor of about 55 compared to previously known methods in characteristic 3, and attain performance comparable to that of RSA in larger characteristics.We also propose faster algorithms for scalar multiplication in characteristic 3 and square root extraction over Fpm, the latter technique being also useful in contexts other than that of pairing-based cryptography

Multilevel kohonen network learning for clustering problems

Clustering is the procedure of recognising classes of patterns that occur in the environment and assigning each pattern to its relevant class. Unlike classical statistical methods, self-organising map (SOM) does not require any prior knowledge about the statistical distribution of the patterns in the environment. In this study, an alternative classification of self-organising neural networks, known as multilevel learning, was proposed to solve the task of pattern separation. The performance of standard SOM and multilevel SOM were evaluated with different distance or dissimilarity measures in retrieving similarity between patterns. The purpose of this analysis was to evaluate the quality of map produced by SOM learning using different distance measures in representing a given dataset. Based on the results obtained from both SOM methods, predictions can be made for the unknown samples. The results showed that multilevel SOM learning gives better classification rate for small and medium scale datasets, but not for large scale dataset

P versus NP and geometry

I describe three geometric approaches to resolving variants of P v. NP, present several results that illustrate the role of group actions in complexity theory, and make a first step towards completely geometric definitions of complexity classes.Comment: 20 pages, to appear in special issue of J. Symbolic. Comp. dedicated to MEGA 200

Computational and Energy Costs of Cryptographic Algorithms on Handheld Devices

Networks are evolving toward a ubiquitous model in which heterogeneous devices are interconnected. Cryptographic algorithms are required for developing security solutions that protect network activity. However, the computational and energy limitations of network devices jeopardize the actual implementation of such mechanisms. In this paper, we perform a wide analysis on the expenses of launching symmetric and asymmetric cryptographic algorithms, hash chain functions, elliptic curves cryptography and pairing based cryptography on personal agendas, and compare them with the costs of basic operating system functions. Results show that although cryptographic power costs are high and such operations shall be restricted in time, they are not the main limiting factor of the autonomy of a device

Experimental Progress in Computation by Self-Assembly of DNA Tilings

Approaches to DNA-based computing by self-assembly require the use of D. T A nanostructures, called tiles, that have efficient chemistries, expressive computational power: and convenient input and output (I/O) mechanisms. We have designed two new classes of DNA tiles: TAO and TAE, both of which contain three double-helices linked by strand exchange. Structural analysis of a TAO molecule has shown that the molecule assembles efficiently from its four component strands. Here we demonstrate a novel method for I/O whereby multiple tiles assemble around a single-stranded (input) scaffold strand. Computation by tiling theoretically results in the formation of structures that contain single-stranded (output) reported strands, which can then be isolated for subsequent steps of computation if necessary. We illustrate the advantages of TAO and TAE designs by detailing two examples of massively parallel arithmetic: construction of complete XOR and addition tables by linear assemblies of DNA tiles. The three helix structures provide flexibility for topological routing of strands in the computation: allowing the implementation of string tile models

Privacy Leakages in Approximate Adders

Approximate computing has recently emerged as a promising method to meet the low power requirements of digital designs. The erroneous outputs produced in approximate computing can be partially a function of each chip's process variation. We show that, in such schemes, the erroneous outputs produced on each chip instance can reveal the identity of the chip that performed the computation, possibly jeopardizing user privacy. In this work, we perform simulation experiments on 32-bit Ripple Carry Adders, Carry Lookahead Adders, and Han-Carlson Adders running at over-scaled operating points. Our results show that identification is possible, we contrast the identifiability of each type of adder, and we quantify how success of identification varies with the extent of over-scaling and noise. Our results are the first to show that approximate digital computations may compromise privacy. Designers of future approximate computing systems should be aware of the possible privacy leakages and decide whether mitigation is warranted in their application.Comment: 2017 IEEE International Symposium on Circuits and Systems (ISCAS

Language control is not a one-size-fits-all languages process: Evidence from simultaneous interpretation students and the n-2 repetition cost

Simultaneous interpretation is an impressive cognitive feat which necessitates the simultaneous use of two languages and therefore begs the question: how is language management accomplished during interpretation? One possibility is that both languages are maintained active and inhibitory control is reduced. To examine whether inhibitory control is reduced after experience with interpretation, students with varying experience were assessed on a three language switching paradigm. This paradigm provides an empirical measure of the inhibition applied to abandoned languages, the n-2 repetition cost. The groups showed different patterns of n-2 repetition costs across the three languages. These differences, however, were not connected to experience with interpretation. Instead, they may be due to other language characteristics. Specifically, the L2 n-2 repetition cost negatively correlated with self-rated oral L2 proficiency, suggesting that language proficiency may affect the use of inhibitory control. The differences seen in the L1 n-2 repetition cost, alternatively, may be due to the differing predominant interactional contexts of the groups. These results suggest that language control may be more complex than previously thought, with different mechanisms used for different languages. Further, these data represent the first use of the n-2 repetition cost as a measure to compare language control between groups