Delphine Christin

A survey on privacy in mobile participatory sensing applications

Abstract: The presence of multimodal sensors on current mobile phones enables a broad range of novel mobile applications. Environmental and user-centric sensor data of unprecedented quantity and quality can be captured and reported by a possible user base of billions of mobile phone subscribers worldwide. The strong focus on the collection of detailed sensor data may however compromise user privacy in various regards, e.g., by tracking a users current location. In this survey, we identify the sensing modalities used in current participatory sensing applications, and assess the threats to user privacy when personal information is sensed and disclosed. We outline how privacy aspects are addressed in existing sensing applications, and determine the adequacy of the solutions under real-world conditions. Finally, we present countermeasures from related research fields, and discuss their applicability in participatory sensing scenarios. Based on our findings, we identify open issues and outline possible solutions to guarantee user privacy in participatory sensing.

Survey on Wireless Sensor Network Technologies for Industrial Automation: The Security and Quality of Service Perspectives

Wireless Sensor Networks (WSNs) are gradually adopted in the industrial world due to their advantages over wired networks. In addition to saving cabling costs, WSNs widen the realm of environments feasible for monitoring. They thus add sensing and acting capabilities to objects in the physical world and allow for communication among these objects or with services in the future Internet. However, the acceptance of WSNs by the industrial automation community is impeded by open issues, such as security guarantees and provision of Quality of Service (QoS). To examine both of these perspectives, we select and survey relevant WSN technologies dedicated to industrial automation. We determine QoS requirements and carry out a threat analysis, which act as basis of our evaluation of the current state-of-the-art. According to the results of this evaluation, we identify and discuss open research issues.

IncogniSense: An anonymity-preserving reputation framework for participatory sensing applications

Reputation systems rate the contributions to participatory sensing campaigns from each user by associating a reputation score. The reputation scores are used to weed out incorrect sensor readings. However, an adversary can deanonmyize the users even when they use pseudonyms by linking the reputation scores associated with multiple contributions. Since the contributed readings are usually annotated with spatiotemporal information, this poses a serious breach of privacy for the users. In this paper, we address this privacy threat by proposing a framework called IncogniSense. Our system utilizes periodic pseudonyms generated using blind signature and relies on reputation transfer between these pseudonyms. The reputation transfer process has an inherent trade-off between anonymity protection and loss in reputation. We investigate by means of extensive simulations several reputation cloaking schemes that address this tradeoff in different ways. Our system is robust against reputation corruption and a prototype implementation demonstrates that the associated overheads are minimal.

Privacy-Preserving Collaborative Path Hiding for Participatory Sensing Applications

The presence of multimodal sensors on current mobile phones enables a broad range of novel mobile applications including, e.g., monitoring noise pollution or traffic and road conditions in urban environments. Data of unprecedented quantity and quality can be collected and reported by a possible user base of billions of mobile phone subscribers worldwide. The collection of detailed sensor and location data may however compromise user privacy. In this paper, we present a decentralized mechanism to preserve location privacy during the collection of sensor readings. As most sensor readings are geotagged, we propose to exchange them between users in physical proximity in order to jumble the paths followed by the users. We evaluate different strategies to exchange and report the sensor readings to the application using real-world GPS traces of mobile users. The results demonstrate the feasibility and efficacy of our proposed scheme, which can obfuscate up to 100% of the visited locations in the best instances.

uSafe: A Privacy-aware and Participative Mobile Application for Citizen Safety in Urban Environments

Abstract Recent mobile applications empower citizens to monitor noise pollution or report on features of their urban environment. One important aspect of urban life has, however, not been sufficiently addressed, namely the safety of citizens. We present a privacy-aware application called uSafe, in which users indicate how safe they feel in geographical locations. These feelings are then consolidated into summary maps accessible by other users and urban planners. We evaluate our concept with a questionnaire-based study involving 183 participants. The results confirm the utility of uSafe and show that privacy protection is a decisive factor in their decision to contribute to it.

Trimming the tree: tailoring adaptive huffman coding to wireless sensor networks

Nodes in wireless sensor networks are generally designed to operate on a limited energy budget, and must consciously use the available charge to allow for long lifetimes. As the radio transceiver is the predominant power consumer on current node platforms, the minimization of its activity periods and efficient use of the radio channel are major targets for optimization. Data compression is a viable option to increase the packet information density, resulting in reduced transmission durations and thus allowing for an optimized channel utilization. The computational and memory demands of many current compression algorithms however hamper their applicability on sensor nodes. In this paper, we present a novel variant of the adaptive Huffman coding algorithm, operating on reduced code table sizes and thus significantly alleviating the resource demands for storing and updating the code table during runtime. An implementation for tmote sky hardware proves its adequacy to the capabilities of sensor nodes, and we present its achievable compression gains and energy requirements in both simulation and real world experiments. Results anticipate that overall energy savings can be achieved when transferring packets of reduced sizes, even when increased CPU utilization is incurred.

Impenetrable obscurity vs. informed decisions: privacy solutions for Participatory Sensing

By harnessing sensors embedded in personal end devices, Participatory Sensing enables novel applications, but also raises severe privacy concerns. Instead of using existing centralized privacy mechanisms that remain obscure to the participants, we propose to involve the participants themselves into the process to protect privacy by interacting directly with others users using their available sensors. Furthermore, our decentralized solution helps in limiting the dissemination of sensitive data, which eliminates some threats to privacy.

Predicting the Power Consumption of Electric Appliances through Time Series Pattern Matching

We present a system to forecast the power consumption of electric household appliances. Accurate load prediction has numerous application domains, e.g., the facilitation of peak load prediction at a much higher resolution than permitted by state-of-the-art load profiles. Our solution is based on the identification and isolation of representative characteristic signatures from previously collected power consumption traces. Subsequently, time series pattern matching is applied to detect these signatures in real-time data, and emit predictions of an appliances future consumption based thereupon. We evaluate the prediction accuracy of our approach with thousands of device-level power consumption traces and highlight the achievable prediction horizon.

Do you hear what I hear? Using acoustic probing to detect smartphone locations

Many context-aware smartphone applications depend on specific conditions for gathering data, e.g., specific phone locations or orientations. As a result, the significant overhead of keeping all this information in mind is imposed on their users. Besides averting the interest of potential application users, these requirements defeat one of the main purposes of these mobile data collection, namely simplifying life through mobile sensing applications. This is not a problem that solely affects the users, but the developers of the applications alike. As even the most diligent users often do not manage to follow the strict data collection guidelines at all times, errors in the collected data may ultimately lead to the provision of wrong services and thus to degraded application quality. In this paper, we thus present a solution to determine the location of a phone in order to support context-aware applications. It offers the possibility to detect the position of the phone with an accuracy of 97 %, as well as being able to correlate it with the type of the location of the user. Our system can be used to improve existing mobile sensing applications by facilitating various services that depend on the phone location, e.g., seamlessly adapting the ringtone volume or setting a phones flight mode.

Enhancing user privacy by preprocessing distributed smart meter data

The increasing presence of renewable sources requires power grid operators to continuously monitor electricity generation and demand in order to maintain the grids stability. To this end, smart meters have been deployed to collect realtime information about the current grid load and forward it to the utility in a timely manner. High resolution smart meter data can however reveal the nature of appliances and their mode of operation with high accuracy, and thus endanger user privacy. In this paper, we investigate the impact on user privacy when the consumption data collected by distributed smart metering devices are preprocessed prior to their usage. We therefore assess the impact on the successful classification of appliances when sensor readings are (1) quantized, (2) down-sampled at a lower sampling rate, and (3) averaged by means of an FIR filter. Our evaluation shows that a combination of these preprocessing steps can provide a balanced trade-off that is in the interests of both users (privacy protection) and utilities (near real-time information).