Radiation tests of the Silicon Drift Detectors for LOFT

During the three years long assessment phase of the LOFT mission, candidate to the M3 launch opportunity of the ESA Cosmic Vision programme, we estimated and measured the radiation damage of the silicon drift detectors (SDDs) of the satellite instrumentation. In particular, we irradiated the detectors with protons (of 0.8 and 11 MeV energy) to study the increment of leakage current and the variation of the charge collection efficiency produced by the displacement damage, and we "bombarded" the detectors with hypervelocity dust grains to measure the effect of the debris impacts. In this paper we describe the measurements and discuss the results in the context of the LOFT mission.Comment: Proc. SPIE 9144, Space Telescopes and Instrumentation 2014: Ultraviolet to Gamma Ray, 91446

Machine Learning Models that Remember Too Much

Machine learning (ML) is becoming a commodity. Numerous ML frameworks and services are available to data holders who are not ML experts but want to train predictive models on their data. It is important that ML models trained on sensitive inputs (e.g., personal images or documents) not leak too much information about the training data. We consider a malicious ML provider who supplies model-training code to the data holder, does not observe the training, but then obtains white- or black-box access to the resulting model. In this setting, we design and implement practical algorithms, some of them very similar to standard ML techniques such as regularization and data augmentation, that "memorize" information about the training dataset in the model yet the model is as accurate and predictive as a conventionally trained model. We then explain how the adversary can extract memorized information from the model. We evaluate our techniques on standard ML tasks for image classification (CIFAR10), face recognition (LFW and FaceScrub), and text analysis (20 Newsgroups and IMDB). In all cases, we show how our algorithms create models that have high predictive power yet allow accurate extraction of subsets of their training data

ConXsense - Automated Context Classification for Context-Aware Access Control

We present ConXsense, the first framework for context-aware access control on mobile devices based on context classification. Previous context-aware access control systems often require users to laboriously specify detailed policies or they rely on pre-defined policies not adequately reflecting the true preferences of users. We present the design and implementation of a context-aware framework that uses a probabilistic approach to overcome these deficiencies. The framework utilizes context sensing and machine learning to automatically classify contexts according to their security and privacy-related properties. We apply the framework to two important smartphone-related use cases: protection against device misuse using a dynamic device lock and protection against sensory malware. We ground our analysis on a sociological survey examining the perceptions and concerns of users related to contextual smartphone security and analyze the effectiveness of our approach with real-world context data. We also demonstrate the integration of our framework with the FlaskDroid architecture for fine-grained access control enforcement on the Android platform.Comment: Recipient of the Best Paper Awar

Accelerator experiments with soft protons and hyper-velocity dust particles: application to ongoing projects of future X-ray missions

We report on our activities, currently in progress, aimed at performing accelerator experiments with soft protons and hyper-velocity dust particles. They include tests of different types of X-ray detectors and related components (such as filters) and measurements of scattering of soft protons and hyper-velocity dust particles off X-ray mirror shells. These activities have been identified as a goal in the context of a number of ongoing space projects in order to assess the risk posed by environmental radiation and dust and qualify the adopted instrumentation with respect to possible damage or performance degradation. In this paper we focus on tests for the Silicon Drift Detectors (SDDs) used aboard the LOFT space mission. We use the Van de Graaff accelerators at the University of T\"ubingen and at the Max Planck Institute for Nuclear Physics (MPIK) in Heidelberg, for soft proton and hyper-velocity dust tests respectively. We present the experimental set-up adopted to perform the tests, status of the activities and some very preliminary results achieved at present time.Comment: Proceedings of SPIE, Vol. 8443, Paper No. 8443-24, 201

Trust and distrust in contradictory information transmission

We analyse the problem of contradictory information distribution in networks of agents with positive and negative trust. The networks of interest are built by ranked agents with different epistemic attitudes. In this context, positive trust is a property of the communication between agents required when message passing is executed bottom-up in the hierarchy, or as a result of a sceptic agent checking information. These two situations are associated with a confirmation procedure that has an epistemic cost. Negative trust results from refusing verification, either of contradictory information or because of a lazy attitude. We offer first a natural deduction system called SecureNDsim to model these interactions and consider some meta-theoretical properties of its derivations. We then implement it in a NetLogo simulation to test experimentally its formal properties. Our analysis concerns in particular: conditions for consensus-reaching transmissions; epistemic costs induced by confirmation and rejection operations; the influence of ranking of the initially labelled nodes on consensus and costs; complexity results

A New View on Interstellar Dust - High Fidelity Studies of Interstellar Dust Analogue Tracks in Stardust Flight Spare Aerogel

In 2000 and 2002 the Stardust Mission exposed aerogel collector panels for a total of about 200 days to the stream of interstellar grains sweeping through the solar system. The material was brought back to Earth in 2006. The goal of this work is the laboratory calibration of the collection process by shooting high speed [5 - 30km/s] interstellar dust (ISD) analogues onto Stardust aerogel flight spares. This enables an investigation into both the morphology of impact tracks as well as any structural and chemical modification of projectile and collector material. First results indicate a different ISD flux than previously assumed for the Stardust collection period

Characteriastion of analogue MAPS produced in the 65 nm TPSCo process

Within the context of the ALICE ITS3 collaboration, a set of MAPS small-scale test structures were developed using the 65 nm TPSCo CMOS imaging process with the upgrade of the ALICE inner tracking system as its primary focus. One such sensor, the Circuit Exploratoire 65 nm (CE-65), and its evolution the CE-65v2, were developed to explore charge collection properties for varying configurations including collection layer process (standard, blanket, modified with gap), pixel pitch (15, 18, 22.5 μm), and pixel geometry (square vs hexagonal/staggered). In this work the characterisation of the CE-65v2 chip, based on 55Fe lab measurements and test beams at CERN SPS, is presented. Matrix gain uniformity up to the O(5%) level was demonstrated for all considered chip configurations. The CE-65v2 chip achieves a spatial resolution of under 2 μm during beam tests. Process modifications allowing for faster charge collection and less charge sharing result in decreased spatial resolution, but a considerably wider range of operation, with both the 15 μm and 22.5 μm chips achieving over 99% efficiency up to a ∼180 e− seed threshold. The results serve to validate the 65 nm TPSCo CMOS process, as well as to motivate design choices in future particle detection experiments