A Block Oriented Fingerprinting Scheme in Relational Database

The need for protecting rights over relational data is of ever increasing concern. There have recently been some pioneering works in this area. In this paper, we propose an effective fingerprinting scheme based on the idea of block method in the area of multimedia fingerprinting. The scheme ensures that certain bit positions of the data contain specific values. The bit positions are determined by the keys known only to the owner of the data and different buyers of the database have different bit positions and different specific values for those bit positions. The detection of the fingerprint can be completed even with a small subset of a marked relation in case that the sample contains the fingerprint. Our extensive analysis shows that the proposed scheme is robust against various forms of attacks, including adding, deleting, shuffling or modifying tuples or attributes and colluding with other recipients of a relation, and ensures the integrity of relation at the same time. ? Springer-Verlag Berlin Heidelberg 2005.EI

Electromagnetic Transmission of Intellectual Property Data to Protect FPGA Designs

International audienceOver the past 10 years, the designers of intellectual properties(IP) have faced increasing threats including cloning, counterfeiting, andreverse-engineering. This is now a critical issue for the microelectronicsindustry. The design of a secure, efficient, lightweight protection scheme fordesign data is a serious challenge for the hardware security community. In thiscontext, this chapter presents two ultra-lightweight transmitters using sidechannel leakage based on electromagnetic emanation to send embedded IPidentity discreetly and quickl

Computation of mutual information from Hidden Markov Models.

Understanding evolution at the sequence level is one of the major research visions of bioinformatics. To this end, several abstract models--such as Hidden Markov Models--and several quantitative measures--such as the mutual information--have been introduced, thoroughly investigated, and applied to several concrete studies in molecular biology. With this contribution we want to undertake a first step to merge these approaches (models and measures) for easy and immediate computation, e.g. for a database of a large number of externally fitted models (such as PFAM). Being able to compute such measures is of paramount importance in data mining, model development, and model comparison. Here we describe how one can efficiently compute the mutual information of a homogenous Hidden Markov Model orders of magnitude faster than with a naive, straight-forward approach. In addition, our algorithm avoids sampling issues of real-world sequences, thus allowing for direct comparison of various models. We applied the method to genomic sequences and discuss properties as well as convergence issues

A fully generic approach for realizing the adaptive web

It is time for Adaptive Web (server) extensions to grow up and become generic. The GRAPPLE (EU FP7) project aimed at integrating Learning Management Systems (LMS) with Adaptive Learning Environments (ALE) in order to support life-long learning. But instead of developing a dedicated Web-based ALE we developed an architecture containing a fully generic adaptive Web server, a distributed User Modeling Framework and a generic browser-based authoring environment for Domain Models and Conceptual Adaptation Models. The GRAPPLE architecture can be used for creating and serving any type of adaptive Web-based application. It supports content-, link- and presentation (layout) adaptation based (in any programmable way) on any desired user model information. In this paper we concentrate on GALE, the adaptation engine we renamed to the "Generic Adaptation Language and Engine". We describe the key elements that make GALE into a truly generic and highly extensible Web-based adaptive delivery environment