Exploratory Study of the Privacy Extension for System Theoretic Process Analysis (STPA-Priv) to elicit Privacy Risks in eHealth

Context: System Theoretic Process Analysis for Privacy (STPA-Priv) is a novel privacy risk elicitation method using a top down approach. It has not gotten very much attention but may offer a convenient structured approach and generation of additional artifacts compared to other methods. Aim: The aim of this exploratory study is to find out what benefits the privacy risk elicitation method STPA-Priv has and to explain how the method can be used. Method: Therefore we apply STPA-Priv to a real world health scenario that involves a smart glucose measurement device used by children. Different kinds of data from the smart device including location data should be shared with the parents, physicians, and urban planners. This makes it a sociotechnical system that offers adequate and complex privacy risks to be found. Results: We find out that STPA-Priv is a structured method for privacy analysis and finds complex privacy risks. The method is supported by a tool called XSTAMPP which makes the analysis and its results more profound. Additionally, we learn that an iterative application of the steps might be necessary to find more privacy risks when more information about the system is available later. Conclusions: STPA-Priv helps to identify complex privacy risks that are derived from sociotechnical interactions in a system. It also outputs privacy constraints that are to be enforced by the system to ensure privacy.Comment: author's post-prin

A Middleware for the Internet of Things

The Internet of Things (IoT) connects everyday objects including a vast array of sensors, actuators, and smart devices, referred to as things to the Internet, in an intelligent and pervasive fashion. This connectivity gives rise to the possibility of using the tracking capabilities of things to impinge on the location privacy of users. Most of the existing management and location privacy protection solutions do not consider the low-cost and low-power requirements of things, or, they do not account for the heterogeneity, scalability, or autonomy of communications supported in the IoT. Moreover, these traditional solutions do not consider the case where a user wishes to control the granularity of the disclosed information based on the context of their use (e.g. based on the time or the current location of the user). To fill this gap, a middleware, referred to as the Internet of Things Management Platform (IoT-MP) is proposed in this paper.Comment: 20 pages, International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.2, March 201

Potential mass surveillance and privacy violations in proximity-based social applications

Proximity-based social applications let users interact with people that are currently close to them, by revealing some information about their preferences and whereabouts. This information is acquired through passive geo-localisation and used to build a sense of serendipitous discovery of people, places and interests. Unfortunately, while this class of applications opens different interactions possibilities for people in urban settings, obtaining access to certain identity information could lead a possible privacy attacker to identify and follow a user in their movements in a specific period of time. The same information shared through the platform could also help an attacker to link the victim's online profiles to physical identities. We analyse a set of popular dating application that shares users relative distances within a certain radius and show how, by using the information shared on these platforms, it is possible to formalise a multilateration attack, able to identify the user actual position. The same attack can also be used to follow a user in all their movements within a certain period of time, therefore identifying their habits and Points of Interest across the city. Furthermore we introduce a social attack which uses common Facebook likes to profile a person and finally identify their real identity

Semi-autonomous, context-aware, agent using behaviour modelling and reputation systems to authorize data operation in the Internet of Things

In this paper we address the issue of gathering the "informed consent" of an end user in the Internet of Things. We start by evaluating the legal importance and some of the problems linked with this notion of informed consent in the specific context of the Internet of Things. From this assessment we propose an approach based on a semi-autonomous, rule based agent that centralize all authorization decisions on the personal data of a user and that is able to take decision on his behalf. We complete this initial agent by integrating context-awareness, behavior modeling and community based reputation system in the algorithm of the agent. The resulting system is a "smart" application, the "privacy butler" that can handle data operations on behalf of the end-user while keeping the user in control. We finally discuss some of the potential problems and improvements of the system.Comment: This work is currently supported by the BUTLER Project co-financed under the 7th framework program of the European Commission. published in Internet of Things (WF-IoT), 2014 IEEE World Forum, 6-8 March 2014, Seoul, P411-416, DOI: 10.1109/WF-IoT.2014.6803201, INSPEC: 1425565