2,638 research outputs found
Asymmetric Leakage from Multiplier and Collision-Based Single-Shot Side-Channel Attack
The single-shot collision attack on RSA proposed by Hanley et al. is studied focusing on the difference between two operands of multiplier. It is shown that how leakage from integer multiplier and long-integer multiplication algorithm can be asymmetric between two operands. The asymmetric leakage is verified with experiments on FPGA and micro-controller platforms. Moreover, we show an experimental result in which success and failure of the attack is determined by the order of operands. Therefore, designing operand order can be a cost-effective countermeasure. Meanwhile we also show a case in which a particular countermeasure becomes ineffective when the asymmetric leakage is considered. In addition to the above main contribution, an extension of the attack by Hanley et al. using the signal-processing technique of Big Mac Attack is presented
Computing Petaflops over Terabytes of Data: The Case of Genome-Wide Association Studies
In many scientific and engineering applications, one has to solve not one but
a sequence of instances of the same problem. Often times, the problems in the
sequence are linked in a way that allows intermediate results to be reused. A
characteristic example for this class of applications is given by the
Genome-Wide Association Studies (GWAS), a widely spread tool in computational
biology. GWAS entails the solution of up to trillions () of correlated
generalized least-squares problems, posing a daunting challenge: the
performance of petaflops ( floating-point operations) over terabytes
of data.
In this paper, we design an algorithm for performing GWAS on multi-core
architectures. This is accomplished in three steps. First, we show how to
exploit the relation among successive problems, thus reducing the overall
computational complexity. Then, through an analysis of the required data
transfers, we identify how to eliminate any overhead due to input/output
operations. Finally, we study how to decompose computation into tasks to be
distributed among the available cores, to attain high performance and
scalability. With our algorithm, a GWAS that currently requires the use of a
supercomputer may now be performed in matter of hours on a single multi-core
node.
The discussion centers around the methodology to develop the algorithm rather
than the specific application. We believe the paper contributes valuable
guidelines of general applicability for computational scientists on how to
develop and optimize numerical algorithms
Data degradation to enhance privacy for the Ambient Intelligence
Increasing research in ubiquitous computing techniques towards the development of an Ambient Intelligence raises issues regarding privacy. To gain the required data needed to enable application in this Ambient Intelligence to offer smart services to users, sensors will monitor users' behavior to fill personal context histories. Those context histories will be stored on database/information systems which we consider as honest: they can be trusted now, but might be subject to attacks in the future. Making this assumption implies that protecting context histories by means of access control might be not enough. To reduce the impact of possible attacks, we propose to use limited retention techniques. In our approach, we present applications a degraded set of data with a retention delay attached to it which matches both application requirements and users privacy wishes. Data degradation can be twofold: the accuracy of context data can be lowered such that the less privacy sensitive parts are retained, and context data can be transformed such that only particular abilities for application remain available. Retention periods can be specified to trigger irreversible removal of the context data from the system
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