Privacy Challenges in the Quantified Self Movement – An EU Perspective

Open access


The gathering of data about oneself (such as running speed, pulse, breathing rate, food consumption, etc.) is rapidly becoming more popular, and has lead to the catch phrase “Quantified Self” (QS). While this trend creates opportunities both for individuals and for society, it also creates risks, due to the data’s personal and often sensitive nature. Countering these risks, while keeping the benefits of QS services, is a task both for the legal system and for the technical community. However, it should also take users’ expectations into account. We therefore analyze the legal situation of QS services based on European law and the privacy policies of some major service providers to clarify the practical consequences for users. We present the result of a study concerning the users’ views on privacy, revealing a conflict between the user’s expectations and the providers’ practices. To help resolve the conflict, we discuss how existing and future privacy-enhancing technologies can avoid the risks associated with QS services.

[1] adidas Group. Adidas Group acquires Runtastic., Aug. 2015. Press Release.

[2] I. Ajzen. The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2):179-211, 1991. Theories of Cognitive Self-Regulation.

[3] Apple Inc. Developer: HealthKit. Webpage.

[4] M. Baldauf, S. Dustdar, and F. Rosenberg. A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing, 2(4):263-277, 2007.

[5] M. B. Barcena, C. Wueest, and H. Lau. How safe is your quantified self? Technical report, Symantec, Aug. 2014. Version 1.1.

[6] M. Borghi, F. Ferretti, and S. Karapapa. Online data processing consent under EU law: a theoretical framework and empirical evidence from the UK. International Journal of Law and Information Technology, 21(2):109-153, 2013.

[7] J. Camenisch and A. Lysyanskaya. Signature Schemes and Anonymous Credentials from Bilinear Maps. In M. K. Franklin, editor, Advances in Cryptology - CRYPTO 2004, 24th Annual International Cryptology Conference, Santa Barbara, California, USA, August 15-19, 2004, Proceedings, volume 3152 of Lecture Notes in Computer Science, pages 56-72. Springer, 2004.

[8] M. Conner and C. J. Armitage. Extending the Theory of Planned Behavior: A Review and Avenues for Further Research. Journal of Applied Social Psychology, 28(15):1429-1464, 1998.

[9] A. Daly. The law and ethics of ‘self-quantified’ health information: an australian perspective. International Data Privacy Law, 5(2):144-155, 2015.

[10] R. Dingledine, N. Mathewson, and P. F. Syverson. Tor: The Second-Generation Onion Router. In M. Blaze, editor, Proceedings of the 13th USENIX Security Symposium, August 9-13, 2004, San Diego, CA, USA, pages 303-320. USENIX, 2004.

[11] Eugene Mandel. How the Napa Earthquake affected Bay Area sleepers. Webpage, August 2014.

[12] Flurry. Health and Fitness Apps Finally Take Off, Fueled by Fitness Fanatics. Webpage, June 2014.

[13] M. Freedman, K. Nissim, and B. Pinkas. Efficient private matching and set intersection. In C. Cachin and J. Camenisch, editors, Advances in Cryptology - EUROCRYPT 2004, volume 3027 of Lecture Notes in Computer Science, pages 1-19. Springer Berlin Heidelberg, 2004.

[14] J. Girao, D. Westhoff, and M. Schneider. CDA: concealed data aggregation for reverse multicast traffic in wireless sensor networks. In 2005 IEEE International Conference on Communications, 2005, volume 5, pages 3044-3049 Vol. 5, May 2005.

[15] P. Gola, C. Klug, B. Körffer, and R. Schomerus. BDSG: Bundesdatenschutzgesetz: Kommentar. C.H.Beck, 11th edition, 2012.

[16] J. Grossklags and A. Acquisti. When 25 Cents is Too Much: An Experiment on Willingness-To-Sell and Willingness-To- Protect Personal Information. In 6th Annual Workshop on the Economics of Information Security, WEIS 2007, 2007.

[17] M. Gruteser and B. Hoh. On the Anonymity of Periodic Location Samples. In D. Hutter and M. Ullmann, editors, Security in Pervasive Computing, volume 3450 of Lecture Notes in Computer Science, pages 179-192. Springer Berlin Heidelberg, 2005.

[18] A. Hilts, C. Parsons, and J. Knockel. Every step you fake - a comparative analysis of fitness tracker privacy and security. Technical report, Open Effect, 2016. Version 0.3.

[19] R. Hoyle, R. Templeman, S. Armes, D. Anthony, D. Crandall, and A. Kapadia. Privacy behaviors of lifeloggers using wearable cameras. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp ’14, pages 571-582, New York, NY, USA, 2014. ACM.

[20] M. Humbert, E. Ayday, J.-P. Hubaux, and A. Telenti. Addressing the concerns of the lacks family: Quantification of kin genomic privacy. In Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, CCS ’13, pages 1141-1152, New York, NY, USA, 2013. ACM.

[21] C. Jensen and C. Potts. Privacy Policies As Decision-making Tools: An Evaluation of Online Privacy Notices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’04, pages 471-478, New York, NY, USA, 2004. ACM.

[22] C. Jensen, C. Potts, and C. Jensen. Privacy practices of internet users: Self-reports versus observed behavior. International Journal of Human-Computer Studies, 63(1-2):203-227, 2005. {HCI} research in privacy and security.

[23] J. Kahn. RunKeeper, Withings, Strava, & iHealth plan HealthKit integration, excited for medical industry tie-in. Webpage, June 2014.

[24] J. Kaye. Abandoning informed consent. In O. Corrigan and R. Tutton, editors, Genetic Databases: Socio-Ethical Issues in the Collection and Use of DNA. Routledge, Abingdon, 2004.

[25] A. D. I. Kramer, J. E. Guillory, and J. T. Hancock. Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24):8788-8790, 2014.

[26] B. Latré, B. Braem, I. Moerman, C. Blondia, and P. Demeester. A survey on wireless body area networks. Wirel. Netw., 17(1):1-18, Jan. 2011.

[27] F. G. Martinez Perez, C. Sorge, R. Petrlic, O. Ugus, D. Westhoff, and Gregorio. Privacy Enhanced Architecture for Smart Metering. International Journal of Information Security, 12(2):67-82, 2013.

[28] Quantified Self Meetups.

[29] A. Narayanan and V. Shmatikov. Robust De-anonymization of Large Sparse Datasets. In Proc. IEEE Symposium on Security and Privacy (SP 2008), pages 111-125, May 2008.

[30] Guide to Self-Tracking Tools.

[31] Reuters. Google unveils “Fit” health, fitness tracking platform.

[32] D. Riboni, L. Pareschi, and C. Bettini. Privacy in georeferenced context-aware services: A survey. In C. Bettini, S. Jajodia, P. Samarati, and X. Wang, editors, Privacy in Location-Based Applications, volume 5599 of Lecture Notes in Computer Science, pages 151-172. Springer Berlin Heidelberg, 2009.

[33] runtastic GmbH. Facts About Runtastic. Available at, May 2016.

[34] Samsung. Intelligence for smarter health. Webpage.

[35] P. M. Schwartz. The eu-u.s. privacy collision: A turn to institutions and procedures. Harvard Law Review, 126:1966-2009, 2013.

[36] J. Y. Tsai, S. Egelman, L. Cranor, and A. Acquisti. The effect of online privacy information on purchasing behavior: An experimental study. Info. Sys. Research, 22(2):254-268, June 2011.

[37] G. Wolf. What is The Quantified Self., Mar. 2011.

[38] C. K. Wong, M. Gouda, and S. S. Lam. Secure group communications using key graphs. In Proceedings of the ACM SIGCOMM ’98 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, SIGCOMM ’98, pages 68-79, New York, NY, USA, 1998. ACM.

[39] A. C. Yao. Protocols for Secure Computations. In Proceedings of the 23rd Annual Symposium on Foundations of Computer Science, pages 160-164, Washington, DC, USA, 1982. IEEE Computer Society.

[40] J. Zhang, V. Dibia, A. Sodnomov, and P. B. Lowry. Understanding the disclosure of private healthcare information within online quantified self 2.0 platforms. In 19th Pacific Asia Conference on Information Systems, PACIS 2015, Singapore, July 5-9, 2015, page 140, 2015.

Journal Information


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 234 234 39
PDF Downloads 92 92 21