Supporting Location Privacy Management through Feedback and Control

Participation in modern, socially-focused digital systems involves a large degree of privacy management, i.e. controlling who may access what information under what circumstances. Effective privacy management (control) requires that mobile systems’ users be able to make informed privacy decisions as their experience and knowledge of a system progresses. By informed, we mean users be aware of the actual information flow. Moreover, privacy preferences vary across the context and it is hard to define privacy policy that reflects the dynamic nature of our lives. This research explores the problem of supporting awareness of information flow and designing usable interfaces for maintaining privacy policies ad-hoc. We borrow from the world of Computer Supported Collaborative Work (CSCW) and propose to incorporate social translucence, a design approach that “supports coherent behaviour by making participants and their activities visible to one another”. We use the characteristics of social translucence, namely visibility, awareness and accountability in order to introduce social norms in spatially dispersed systems. Our research is driven by two questions: (1) how can artifacts from real world social interaction, such as responsibility, be embedded into mobile interaction; and (2) can systems be designed in which both privacy violations and the burden of privacy management is minimized. The contributions of our work are: (1) an implementation of Buddy Tracker, privacy-aware location-sharing application based on the social translucence; (2) the design and evaluation of the concept of real-time feedback as a means of incorporating social translucence in location-sharing scenarios; and finally (3) a novel interface for ad-hoc privacy management called Privacy-Shake. We explore the role of real-time feedback for privacy management in the context of Buddy Tracker. Informed by focus group discussions, interviews, surveys and two field trials of Buddy Tracker we found that when using a system that provided real-time feedback, people were more accountable for their actions and reduced the number of unreasonable location requests. From our observations we develop concrete design guidelines for incorporating real-time feedback into information sharing applications in a manner that ensures social acceptance of the technology

Efficient Algorithms for Searching the Minimum Information Partition in Integrated Information Theory

The ability to integrate information in the brain is considered to be an essential property for cognition and consciousness. Integrated Information Theory (IIT) hypothesizes that the amount of integrated information ($\Phi$) in the brain is related to the level of consciousness. IIT proposes that to quantify information integration in a system as a whole, integrated information should be measured across the partition of the system at which information loss caused by partitioning is minimized, called the Minimum Information Partition (MIP). The computational cost for exhaustively searching for the MIP grows exponentially with system size, making it difficult to apply IIT to real neural data. It has been previously shown that if a measure of $\Phi$ satisfies a mathematical property, submodularity, the MIP can be found in a polynomial order by an optimization algorithm. However, although the first version of $\Phi$ is submodular, the later versions are not. In this study, we empirically explore to what extent the algorithm can be applied to the non-submodular measures of $\Phi$ by evaluating the accuracy of the algorithm in simulated data and real neural data. We find that the algorithm identifies the MIP in a nearly perfect manner even for the non-submodular measures. Our results show that the algorithm allows us to measure $\Phi$ in large systems within a practical amount of time

Finish Them!: Pricing Algorithms for Human Computation

Given a batch of human computation tasks, a commonly ignored aspect is how the price (i.e., the reward paid to human workers) of these tasks must be set or varied in order to meet latency or cost constraints. Often, the price is set up-front and not modified, leading to either a much higher monetary cost than needed (if the price is set too high), or to a much larger latency than expected (if the price is set too low). Leveraging a pricing model from prior work, we develop algorithms to optimally set and then vary price over time in order to meet a (a) user-specified deadline while minimizing total monetary cost (b) user-specified monetary budget constraint while minimizing total elapsed time. We leverage techniques from decision theory (specifically, Markov Decision Processes) for both these problems, and demonstrate that our techniques lead to upto 30\% reduction in cost over schemes proposed in prior work. Furthermore, we develop techniques to speed-up the computation, enabling users to leverage the price setting algorithms on-the-fly