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

Can agricultural citizen science improve seed systems? The contributions of crowdsourcing participatory variety selection through on-farm triadic comparisons of technologies

Using on-farm triadic comparisons of technologies (tricot) for crowdsourcing participatory variety selection is a new citizen–science methodology for agriculture. Developed by Bioversity International as part of a programme known as Seeds for Needs, it allows large numbers of farmers to test different technologies on their farms. Farmers receive packages of seeds with a different combination of three different varieties, randomly selected from a large and diverse set of varieties to be tested. They submit their feedback in simple format, ranking the ‘best / middle / worst’ of each package for different traits. These farmer-generated data are then combined with environmental and socioeconomic data and analyzed with specific, novel statistical methods for ranking. Based on a review of several years of crowdsourcing experience in countries around the world, this report summarizes the different features and contributions of the tricot methodology to improve the functionality of seed systems

Crowdsourced intuitive visual design feedback

For many people images are a medium preferable to text and yet, with the exception of star ratings, most formats for conventional computer mediated feedback focus on text. This thesis develops a new method of crowd feedback for designers based on images. Visual summaries are generated from a crowd’s feedback images chosen in response to a design. The summaries provide the designer with impressionistic and inspiring visual feedback. The thesis sets out the motivation for this new method, describes the development of perceptually organised image sets and a summarisation algorithm to implement it. Evaluation studies are reported which, through a mixed methods approach, provide evidence of the validity and potential of the new image-based feedback method. It is concluded that the visual feedback method would be more appealing than text for that section of the population who may be of a visual cognitive style. Indeed the evaluation studies are evidence that such users believe images are as good as text when communicating their emotional reaction about a design. Designer participants reported being inspired by the visual feedback where, comparably, they were not inspired by text. They also reported that the feedback can represent the perceived mood in their designs, and that they would be enthusiastic users of a service offering this new form of visual design feedback

Incentive mechanism design for mobile crowd sensing systems

The recent proliferation of increasingly capable and affordable mobile devices with a plethora of on-board and portable sensors that pervade every corner of the world has given rise to the fast development and wide deployment of mobile crowd sensing (MCS) systems. Nowadays, applications of MCS systems have covered almost every aspect of people's everyday living and working, such as ambient environment monitoring, healthcare, floor plan reconstruction, smart transportation, indoor localization, and many others. Despite their tremendous benefits, MCS systems pose great new research challenges, of which, this thesis targets one important facet, that is, to effectively incentivize (crowd) workers to achieve maximum participation in MCS systems. Participating in crowd sensing tasks is usually a costly procedure for individual workers. On one hand, it consumes workers' resources, such as computing power, battery, and so forth. On the other hand, a considerable portion of sensing tasks require the submission of workers' sensitive and private information, which causes privacy leakage for participants. Clearly, the power of crowd sensing could not be fully unleashed, unless workers are properly incentivized to participate via satisfactory rewards that effectively compensate their participation costs. Targeting the above challenge, in this thesis, I present a series of novel incentive mechanisms, which can be utilized to effectively incentivize worker participation in MCS systems. The proposed mechanisms not only incorporate workers' quality of information in order to selectively recruit relatively more reliable workers for sensing, but also preserve workers' privacy so as to prevent workers from being disincentivized by excessive privacy leakage. I demonstrate through rigorous theoretical analyses and extensive simulations that the proposed incentive mechanisms bear many desirable properties theoretically, and have great potential to be practically applied