Dpm

Bio: Dpm is an academic researcher. The author has contributed to research in topics: Information privacy & Security policy. The author has an hindex of 1, co-authored 1 publications receiving 61 citations.

Data privacy management and autonomous spontaneous security : 4th international workshop : DPM 2009, and second international workshop, SETOP 2009, St. Malo, France, September 24 - 25, 2009 : revised selected papers

Abstract: Keynote Talks.- The UNESCO Chair in Data Privacy Research in Vehicular Networks.- Privacy Management for Global Organizations.- Data Privacy Management.- Obligation Language and Framework to Enable Privacy-Aware SOA.- Distributed Privacy-Preserving Methods for Statistical Disclosure Control.- Towards a Privacy-Preserving National Identity Card.- Using SAT-Solvers to Compute Inference-Proof Database Instances.- A Quantitative Analysis of Indistinguishability for a Continuous Domain Biometric Cryptosystem.- A Spatial Cloaking Framework Based on Range Search for Nearest Neighbor Search.- Visualizing Privacy Implications of Access Control Policies in Social Network Systems.- Contextual Privacy Management in Extended Role Based Access Control Model.- Autonomous and Spontaneous Security.- Dynamic Security Rules for Geo Data.- Medical Image Integrity Control Combining Digital Signature and Lossless Watermarking.- ASRBAC: A Security Administration Model for Mobile Autonomic Networks (MAutoNets).- Untraceable Tags Based on Mild Assumptions.- Security Threat Mitigation Trends in Low-Cost RFID Systems.- An Effective TCP/IP Fingerprinting Technique Based on Strange Attractors Classification.- DDoS Defense Mechanisms: A New Taxonomy.- RDyMASS: Reliable and Dynamic Enforcement of Security Policies for Mobile Agent Systems.- Achieving Life-Cycle Compliance of Service-Oriented Architectures: Open Issues and Challenges.

Forgetting personal data and revoking consent under the GDPR: Challenges and proposed solutions

Abstract: Upon the GDPR’s application on 25 May 2018 across the European Union, new legal requirements for the protection of personal data will be enforced for data controllers operating within the EU territory. While the principles encompassed by the GDPR were mostly welcomed, two of them; namely the right to withdraw consent and the right to be forgotten, caused prolonged controversy among privacy scholars, human rights advocates and business world due to their pivotal impact on the way personal data would be handled under the new legal provisions and the drastic consequences of enforcing these new requirements in the era of big data and internet of things. In this work, we firstly review all controversies around the new stringent definitions of consent revocation and the right to be forgotten in reference to their implementation impact on privacy and personal data protection, and secondly, we evaluate existing methods, architectures and state-of-the-art technologies in terms of fulfilling the technical practicalities for the implementation and effective integration of the new requirements into current computing infrastructures. The latter allow us to argue that such enforcement is indeed feasible provided that implementation guidelines and low-level business specifications are put in place in a clear and cross-platform manner in order to cater for all possible exceptions and complexities.

A community assessment of privacy preserving techniques for human genomes

Abstract: To answer the need for the rigorous protection of biomedical data, we organized the Critical Assessment of Data Privacy and Protection initiative as a community effort to evaluate privacy-preserving dissemination techniques for biomedical data. We focused on the challenge of sharing aggregate human genomic data (e.g., allele frequencies) in a way that preserves the privacy of the data donors, without undermining the utility of genome-wide association studies (GWAS) or impeding their dissemination. Specifically, we designed two problems for disseminating the raw data and the analysis outcome, respectively, based on publicly available data from HapMap and from the Personal Genome Project. A total of six teams participated in the challenges. The final results were presented at a workshop of the iDASH (integrating Data for Analysis, 'anonymization,' and SHaring) National Center for Biomedical Computing. We report the results of the challenge and our findings about the current genome privacy protection techniques.

Optimization of Power Analysis Using Neural Network

Abstract: In power analysis, many different statistical methods and power consumption models are used to obtain the value of a secret key from the power traces measured. An interesting method of power analysis based on multi-layer perceptron was presented in [1] claiming a \(90\,\%\) success rate. The theoretical and empirical success rates were determined to be \(80\,\%\) and \(85\,\%\), respectively, which is not sufficient enough. In the paper, we propose and realize an optimization of this power analysis method which improves the success rate to almost \(100\,\%\). The optimization is based on preprocessing the measured power traces using the calculation of the average trace and the subsequent calculation of the difference power traces. In this way, the prepared power patterns were used for neural network training and of course during the attack. This optimization is computationally undemanding compared to other methods of preprocessing usually applied in power analysis, and has a great impact on classification results. In the paper, we compare the results of the optimized method with the original implementation. We highlight positive and also some negative impacts of the optimization on classification results.

Secure and lightweight distance-bounding

Abstract: Distance-bounding is a practical solution aiming to prevent relay attacks. The main challenge when designing such protocols is maintaining their inexpensive cryptographic nature, whilst being able to protect against as many, if not all, of the classical threats posed in their context. Moreover, in distance-bounding, some subtle security shortcomings related to the PRF (pseudorandom function) assumption and ingenious attack techniques based on observing verifiers’ outputs have recently been put forward. Also, the recent terrorist-fraud by Hancke somehow recalls once more the need to account for noisy communications in the security analysis of distance-bounding. In this paper, we attempt to incorporate the lessons taught by these new developments in our distance-bounding protocol design. The result is a new class of protocols, with increasing levels of security, accommodating the latest advances; at the same time, we preserve the lightweight nature of the design throughout the whole class.

SecureMA: protecting participant privacy in genetic association meta-analysis.

Abstract: Motivation: Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data. Results: We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multisite association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across substudy sites, without leaking information on individual participants and site-level association summaries. Availability and implementation: Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly available at http://github.com/XieConnect/SecureMA. Our customized secure computation framework is also publicly available at http://github.com/XieConnect/CircuitService Contact: ude.tlibrednav@nilam.b Supplementary information: Supplementary data are available at Bioinformatics online.