Management of body-sensor data in sports analytic with operative consent

Data from affordable body-sensor devices that monitor personal metrics like heart-rate, weight, and movements are changing how athletes train and perform. Existing sport-analytic tools are, however, mostly monolithic proprietary systems where athletes have little control over how their data is used and managed over time. This paper describes Girji, security centered body-sensor data acquisition and management system that embeds a novel form of an active and operational type of computational consent, which gives athletes a high level of control of how their data can be used. This security architecture is implemented using a novel combination of object capabilities that embed executable code and individual meta-code execution containers for flexible consent policies.

[1]  Håvard D. Johansen,et al.  Combining Video and Player Telemetry for Evidence-based Decisions in Soccer , 2018, icSPORTS.

[2]  Tom Wilsgaard,et al.  Cohort profile: The Tromsø Study , 2011, International journal of epidemiology.

[3]  Arnar Birgisson,et al.  Macaroons: Cookies with Contextual Caveats for Decentralized Authorization in the Cloud , 2014, NDSS.

[4]  Robbert van Renesse,et al.  Secure Abstraction with Code Capabilities , 2013, 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[5]  Carsten Griwodz,et al.  Bagadus: An integrated real-time system for soccer analytics , 2014, ACM Trans. Multim. Comput. Commun. Appl..

[6]  T. Beauchamp,et al.  Principles of biomedical ethics , 1991 .

[7]  Cheng-Hsin Hsu,et al.  GamingAnywhere: The first open source cloud gaming system , 2014, TOMCCAP.

[8]  Pieter Van Gorp,et al.  MyPHRMachines: Lifelong Personal Health Records in the cloud , 2012, 2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS).

[9]  P. Krustrup,et al.  Match performance of high-standard soccer players with special reference to development of fatigue , 2003, Journal of sports sciences.

[10]  Dag Johansen,et al.  Self-Managing Data in the Clouds , 2014, 2014 IEEE International Conference on Cloud Engineering.

[11]  Magnus Stenhaug,et al.  Muithu: Smaller footprint, potentially larger imprint , 2012, Seventh International Conference on Digital Information Management (ICDIM 2012).

[12]  Robbert van Renesse,et al.  A TACOMA retrospective , 2002, Softw. Pract. Exp..

[13]  Jeroen Ooms,et al.  The RAppArmor Package: Enforcing Security Policies in R Using Dynamic Sandboxing on Linux , 2013, ArXiv.

[14]  Deborah Estrin,et al.  Personal data vaults: a locus of control for personal data streams , 2010, CoNEXT.