Health Participatory Sensing Networks for Mobile Device Public Health Data Collection and Intervention

The pervasive availability and increasingly sophisticated functionalities of smartphones and their connected external sensors or wearable devices can provide new data collection capabilities relevant to public health. Current research and commercial efforts have concentrated on sensor-based collection of health data for personal fitness and personal healthcare feedback purposes. However, to date there has not been a detailed investigation of how such smartphones and sensors can be utilized for public health data collection. Unlike most sensing applications, in the case of public health, capturing comprehensive and detailed data is not a necessity, as aggregate data alone is in many cases sufficient for public health purposes. As such, public health data has the characteristic of being capturable whilst still not infringing privacy, as the detailed data of individuals that may allow re-identification is not needed, but rather only aggregate, de-identified and non-unique data for an individual. These types of public health data collection provide the challenge of the need to be flexible enough to answer a range of public health queries, while ensuring the level of detail returned preserves privacy. Additionally, the distribution of public health data collection request and other information to the participants without identifying the individual is a core requirement. An additional requirement for health participatory sensing networks is the ability to perform public health interventions. As with data collection, this needs to be completed in a non-identifying and privacy preserving manner. This thesis proposes a solution to these challenges, whereby a form of query assurance provides private and secure distribution of data collection requests and public health interventions to participants. While an additional, privacy preserving threshold approach to local processing of data prior to submission is used to provide re-identification protection for the participant. The evaluation finds that with manageable overheads, minimal reduction in the detail of collected data and strict communication privacy; privacy and anonymity can be preserved. This is significant for the field of participatory health sensing as a major concern of participants is most often real or perceived privacy risks of contribution

Privacy-Preserved Linkable Social-Physical Data Publication

In this dissertation, we investigate the privacy-preserved data publication problems towards pervasively existing linkable social-physical contents. On the one hand, data publication has been considered as a critical approach to facilitate numerous utilities for individuals, populations, platform owners, and all third-party service providers. On the other hand, the unprecedented adoption of mobile devices and the dramatic development of Internet-of-Thing (IoT) systems have pushed the collection of surrounding physical information among populations to a totally novel stage. The collected contents can provide a fine-grained access to both physical and social aspects of the crowds, which introduces a comprehensively linkable and potentially sensitive information domain. The linkage includes the related index like privacy, utility, and efficiency for sophisticated applications, the inherent correlations among multiple data sources or information dimensions, and the connections among individuals. As the linkage leads to various novel challenges for privacy preservation, there should be a body of novel mechanisms for linkable social-physical data publications. As a result, this dissertation proposes a series of mechanisms for privacy-preserved linkable social-physical data publication. Firstly, we study the publication of physical data where the co-existing useful social proles and the sensitive physical proles of the data should be carefully maintained. Secondly, we investigate the data publication problem jointly considering the privacy preservation, data utility, and resource efficiency for task completion in crowd-sensing systems. Thirdly, we investigate the publication of private contents used for the recommendation, where contents of a user contribute to the recommendation results for others. Fourthly, we study the publications of reviews in local business service systems, where users expect to conceal their frequently visited locations while cooperatively maintain the utility of the whole system. Fifthly, we study the acquisition of privacy-preserved knowledge on cyber-physical social networks, where third-party service providers can derive the community structure without accessing the sensitive social links. We also provide detailed analysis and discussion for proposed mechanisms, and extensively validate their performance via real-world datasets. Both results demonstrate that the proposed mechanisms can properly preserve the privacy while maintaining the data utility. At last, we also propose the future research topics to complete the whole dissertation. The first topic focuses on the privacy preservation towards correlations beneath multiple data sources. The second topic studies more privacy issues for the whole population during data publication, including both the novel threats for related communities, and the disclosure of trends within crowds

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

On (The Lack Of) Location Privacy in Crowdsourcing Applications

Crowdsourcing enables application developers to benefit from large and diverse datasets at a low cost. Specifically, mobile crowdsourcing (MCS) leverages users' devices as sensors to perform geo-located data collection. The collection of geo-located data raises serious privacy concerns for users. Yet, despite the large research body on location privacy-preserving mechanisms (LPPMs), MCS developers implement little to no protection for data collection or publication. To understand this mismatch, we study the performance of existing LPPMs on publicly available data from two mobile crowdsourcing projects. Our results show that well-established defenses are either not applicable or offer little protection in the MCS setting. Additionally, they have a much stronger impact on applications' utility than foreseen in the literature. This is because existing LPPMs, designed with location-based services (LBSs) in mind, are optimized for utility functions based on users' locations, while MCS utility functions depend on the values (e.g., measurements) associated with those locations. We finally outline possible research avenues to facilitate the development of new location privacy solutions that fit the needs of MCS so that the increasing number of such applications do not jeopardize their users' privacy

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management