11,080 research outputs found
Cryptanalysis of Masked Ciphers: A not so Random Idea
A new approach to the security analysis of hardware-oriented masked ciphers against second-order side-channel attacks is developed. By relying on techniques from symmetric-key cryptanalysis, concrete security bounds are obtained in a variant of the probing model that allows the adversary to make only a bounded, but possibly very large, number of measurements. Specifically, it is formally shown how a bounded-query variant of robust probing security can be reduced to the linear cryptanalysis of masked ciphers.
As a result, the compositional issues of higher-order threshold implementations can be overcome without relying on fresh randomness. From a practical point of view, the aforementioned approach makes it possible to transfer many of the desirable properties of first-order threshold implementations, such as their low randomness usage, to the second-order setting. For example, a straightforward application to the block cipher LED results in a masking using less than 700 random bits including the initial sharing. In addition, the cryptanalytic approach introduced in this paper provides additional insight into the design of masked ciphers and allows for a quantifiable trade-off between security and performance
Simple, compact and robust approximate string dictionary
This paper is concerned with practical implementations of approximate string
dictionaries that allow edit errors. In this problem, we have as input a
dictionary of strings of total length over an alphabet of size
. Given a bound and a pattern of length , a query has to
return all the strings of the dictionary which are at edit distance at most
from , where the edit distance between two strings and is defined as
the minimum-cost sequence of edit operations that transform into . The
cost of a sequence of operations is defined as the sum of the costs of the
operations involved in the sequence. In this paper, we assume that each of
these operations has unit cost and consider only three operations: deletion of
one character, insertion of one character and substitution of a character by
another. We present a practical implementation of the data structure we
recently proposed and which works only for one error. We extend the scheme to
. Our implementation has many desirable properties: it has a very
fast and space-efficient building algorithm. The dictionary data structure is
compact and has fast and robust query time. Finally our data structure is
simple to implement as it only uses basic techniques from the literature,
mainly hashing (linear probing and hash signatures) and succinct data
structures (bitvectors supporting rank queries).Comment: Accepted to a journal (19 pages, 2 figures
A Comparative Case Study of HTTP Adaptive Streaming Algorithms in Mobile Networks
HTTP Adaptive Streaming (HAS) techniques are now the dominant solution for
video delivery in mobile networks. Over the past few years, several HAS
algorithms have been introduced in order to improve user quality-of-experience
(QoE) by bit-rate adaptation. Their difference is mainly the required input
information, ranging from network characteristics to application-layer
parameters such as the playback buffer. Interestingly, despite the recent
outburst in scientific papers on the topic, a comprehensive comparative study
of the main algorithm classes is still missing. In this paper we provide such
comparison by evaluating the performance of the state-of-the-art HAS algorithms
per class, based on data from field measurements. We provide a systematic study
of the main QoE factors and the impact of the target buffer level. We conclude
that this target buffer level is a critical classifier for the studied HAS
algorithms. While buffer-based algorithms show superior QoE in most of the
cases, their performance may differ at the low target buffer levels of live
streaming services. Overall, we believe that our findings provide valuable
insight for the design and choice of HAS algorithms according to networks
conditions and service requirements.Comment: 6 page
An Overview of Backtrack Search Satisfiability Algorithms
Propositional Satisfiability (SAT) is often used as the underlying model for a significan
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Multi-aspect, robust, and memory exclusive guest os fingerprinting
Precise fingerprinting of an operating system (OS) is critical to many security and forensics applications in the cloud, such as virtual machine (VM) introspection, penetration testing, guest OS administration, kernel dump analysis, and memory forensics. The existing OS fingerprinting techniques primarily inspect network packets or CPU states, and they all fall short in precision and usability. As the physical memory of a VM always exists in all these applications, in this article, we present OS-Sommelier+, a multi-aspect, memory exclusive approach for precise and robust guest OS fingerprinting in the cloud. It works as follows: given a physical memory dump of a guest OS, OS-Sommelier+ first uses a code hash based approach from kernel code aspect to determine the guest OS version. If code hash approach fails, OS-Sommelier+ then uses a kernel data signature based approach from kernel data aspect to determine the version. We have implemented a prototype system, and tested it with a number of Linux kernels. Our evaluation results show that the code hash approach is faster but can only fingerprint the known kernels, and data signature approach complements the code signature approach and can fingerprint even unknown kernels
Genetic Algorithm Modeling with GPU Parallel Computing Technology
We present a multi-purpose genetic algorithm, designed and implemented with
GPGPU / CUDA parallel computing technology. The model was derived from a
multi-core CPU serial implementation, named GAME, already scientifically
successfully tested and validated on astrophysical massive data classification
problems, through a web application resource (DAMEWARE), specialized in data
mining based on Machine Learning paradigms. Since genetic algorithms are
inherently parallel, the GPGPU computing paradigm has provided an exploit of
the internal training features of the model, permitting a strong optimization
in terms of processing performances and scalability.Comment: 11 pages, 2 figures, refereed proceedings; Neural Nets and
Surroundings, Proceedings of 22nd Italian Workshop on Neural Nets, WIRN 2012;
Smart Innovation, Systems and Technologies, Vol. 19, Springe
PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn
Preserving privacy of users is a key requirement of web-scale analytics and
reporting applications, and has witnessed a renewed focus in light of recent
data breaches and new regulations such as GDPR. We focus on the problem of
computing robust, reliable analytics in a privacy-preserving manner, while
satisfying product requirements. We present PriPeARL, a framework for
privacy-preserving analytics and reporting, inspired by differential privacy.
We describe the overall design and architecture, and the key modeling
components, focusing on the unique challenges associated with privacy,
coverage, utility, and consistency. We perform an experimental study in the
context of ads analytics and reporting at LinkedIn, thereby demonstrating the
tradeoffs between privacy and utility needs, and the applicability of
privacy-preserving mechanisms to real-world data. We also highlight the lessons
learned from the production deployment of our system at LinkedIn.Comment: Conference information: ACM International Conference on Information
and Knowledge Management (CIKM 2018
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