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Contextual proximity detection (or, co-presence detection) is a promising approach to defend against relay attacks in many mobile authentication systems. We present a systematic assessment of co-presence detection in the presence of a contextmanipulating attacker. First, we show that it is feasible to manipulate, consistently control and stabilize the readings of different acoustic and physical environment sensors (and even multiple sensors simultaneously) using low-cost, off-the-shelf equipment. Second, based on these capabilities, we show that an attacker who can manipulate the context gains a significant advantage in defeating context-based co-presence detection. For systems that use multiple sensors, we investigate two sensor fusion approaches based on machine learning techniquesfeatures-fusion and decisions-fusion, and show that both are vulnerable to contextual attacks but the latter approach can be more resistant in some cases. arXiv:1511.00905v1 [cs.CR] 3 Nov 2015 1 ch 2 CP CV Prover (P) (Unattended) Verifier (V)

This paper illustrates the application of mobile and wireless technologies for estimating the severity of Parkinson Disease symptoms, and performing a personalized drug administration to PD patients. The measurements of patient finger pressures on the screen of a smart phone, translated into analogue voltage and digital bits, are taken by an Android App. The computations performed through Fast Fourier Transformations (FFT) and Reaction and Movement time, enable the calculation of the severity of the PD symptoms, which results in an appropriate drug administration for that patient, at the moment when the measurement of patient finger pressures is taken. The novelty of this research is twofold. It allows a high level of personalization in PD treatment and uses modern technologies to bring new solutions in the field of drug administration to PD patients.