Understanding the Valuation of Location Privacy: a Crowdsourcing-Based Approach

The exchange of private information for services or other benefits is a commonplace practice today in the advent of mobile technology. In the case of mobile services, the exchanged commodity is increasingly often spatial location of the user. To decide whether this transaction is beneficial, the user needs to evaluate the exchange value of this commodity. To assess the value users give to their location, and to understand its relationship with location sharing, we conducted a study on a mobile crowdsourcing platform (N=190). We find that users\u27 valuation of location privacy is dependent on the sharing scenario. For instance, when the location is to be shared with an untrusted advertiser, the users require a premium as compensation for their information. Additionally, benefit perception and trust are found to be connected with more frequent location sharing, while perceived risks and privacy concern are associated with sharing one’s location less frequently

Data integration and analysis for circadian medicine

Data integration, data sharing, and standardized analyses are important enablers for data-driven medical research. Circadian medicine is an emerging field with a particularly high need for coordinated and systematic collaboration between researchers from different disciplines. Datasets in circadian medicine are multimodal, ranging from molecular circadian profiles and clinical parameters to physiological measurements and data obtained from (wearable) sensors or reported by patients. Uniquely, data spanning both the time dimension and the spatial dimension (across tissues) are needed to obtain a holistic view of the circadian system. The study of human rhythms in the context of circadian medicine has to confront the heterogeneity of clock properties within and across subjects and our inability to repeatedly obtain relevant biosamples from one subject. This requires informatics solutions for integrating and visualizing relevant data types at various temporal resolutions ranging from milliseconds and seconds to minutes and several hours. Associated challenges range from a lack of standards that can be used to represent all required data in a common interoperable form, to challenges related to data storage, to the need to perform transformations for integrated visualizations, and to privacy issues. The downstream analysis of circadian rhythms requires specialized approaches for the identification, characterization, and discrimination of rhythms. We conclude that circadian medicine research provides an ideal environment for developing innovative methods to address challenges related to the collection, integration, visualization, and analysis of multimodal multidimensional biomedical data.Peer Reviewe

Locate! -When do Users Disclose Location?

ABSTRACT Location information and traces (via tracking) can reveal vast amounts of information about a user: where she lives, works, and even which restaurants or friends she visits. Therefore, this information should be handled with sufficient concern and care. Willingness to disclose one's location is influenced by various factors including who is asking the location and what the reason for the location request is, as well as individual characteristics such as one's privacy concerns. This paper outlines a study aimed at determining the relationship between these factors and users' willingness to share their location with others using a mobile device. To study this, we developed a mobile application that lets the users share their current location with others at various levels of accuracy. Using the application, we ran a field study simulating the communication between the participants and their various contacts. Our results show that mainly the personal, rather than external factors influence the tendency for location disclosure. Users with lower privacy concerns regarding the accuracy of personal information share their location with more accuracy. Also, people who generally feel close with others tend to disclose their location more accurately