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Privacy in Mobile Sensing

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Abstract

In this chapter, we discuss the privacy implications of mobile sensing and modern psycho-social sciences. We aim to raise awareness of the multifaceted nature of privacy, describing the legal, technical and applied aspects in some detail. Not only since the European GDPR, these aspects lead to a broad spectrum of challenges of which data processors cannot be absolved by a simple consent form from their users. Instead appropriate technical and organizational measures should be put in place through a proper privacy engineering process. Throughout the chapter, we illustrate the importance of privacy protection through a set of examples and also technical approaches to address these challenges. We conclude this chapter with an outlook on privacy in mobile sensing, digital phenotyping and, psychoinformatics.

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Notes

  1. 1.

    http://www.europarl.europa.eu/charter/pdf/text_en.pdf.

  2. 2.

    https://www.theguardian.com/world/2018/jan/28/fitness-tracking-app-gives-away-location-of-secret-us-army-bases.

  3. 3.

    https://research.neustar.biz/2014/09/15/riding-with-the-stars-passenger-privacy-in-the-nyc-taxicab-dataset/.

  4. 4.

    https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html.

  5. 5.

    https://web.archive.org/web/20140828024924/http://blog.uber.com/ridesofglory.

  6. 6.

    http://www.wired.com/threatlevel/2009/12/netflix-privacy-lawsuit.

References

  • Al-Momani A, Kargl F, Schmidt R, Bösch C (2018) iRide: a privacy-preserving architecture for self-driving cabs service. In: 2018 IEEE vehicular networking conference (VNC), pp 1–8. https://doi.org/10.1109/VNC.2018.8628378

  • Barbaro M, Zeller T (2006) A face is exposed for AOL searcher no. 4417749. New York Times

    Google Scholar 

  • Douriez M, Doraiswamy H, Freire J, Silva CT (2016) Anonymizing nyc taxi data: does it matter?. In: 2016 IEEE international conference on data science and advanced analytics (DSAA), pp 140–148. https://doi.org/10.1109/DSAA.2016.21

  • Hoepman JH (2014) Privacy design strategies. In: IFIP international information security conference. Springer, pp 446–459

    Google Scholar 

  • Joinson A (1999) Social desirability, anonymity, and internet-based questionnaires. Behav Res Methods Instrum Comput 31(3):433–438

    Article  CAS  Google Scholar 

  • Kargl F, Schaub F, Dietzel S (2010) Mandatory enforcement of privacy policies using trusted computing principles. In: Intelligent information privacy management symposium (Privacy 2010) AAAI. Stanford University, USA

    Google Scholar 

  • Kelman HC (1977) Privacy and research with human beings. J Soc Issues 33(3):169–195

    Google Scholar 

  • Krumpal I (2013) Determinants of social desirability bias in sensitive surveys: a literature review. Qual Quant 47(4):2025–2047. https://doi.org/10.1007/s11135-011-9640-9

    Article  Google Scholar 

  • Lindell Y, Pinkas B (2002) Privacy preserving data mining. J Cryptol 15(3):177–206

    Google Scholar 

  • Narayanan A, Shmatikov V (2008) Robust de-anonymization of large sparse datasets. In: 2008 IEEE symposium on security and privacy (S&P 2008), 18–21 May 2008. California, USA, IEEE Computer Society, Oakland, pp 111–125

    Google Scholar 

  • Narayanan A, Shmatikov V (2009) De-anonymizing social networks. In: 30th IEEE symposium on security and privacy (S&P 2009). California, USA, IEEE Computer Society, Oakland, pp 173–187, 17–20 May 2009

    Google Scholar 

  • Narayanan A, Shmatikov V (2010) Myths and fallacies of “personally identifiable information”. Commun ACM 53(6):24–26

    Article  Google Scholar 

  • Pfitzmann A, Hansen M (2010) A terminology for talking about privacy by data minimization: Anonymity, unlinkability, undetectability, unobservability, pseudonymity, and identity management (v0.34). http://dud.inf.tu-dresden.de/Anon_Terminology.shtml

  • Schaar K (2017) Anpassung von Einwilligungserklärungen für wissenschaftliche Forschungsprojekte. Die informierte Einwilligung nach den Vorgaben der DS-GVO und Ethikrichtlinien. Zeitschrift für Datenschutz 5:213–220

    Google Scholar 

  • Serenko N, Fan L (2013) Patients’ perceptions of privacy and their outcomes in healthcare. Int J Behav Healthc Res 4(2):101–122

    Article  Google Scholar 

  • Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertain Fuzziness Knowl-Based Syst 10(5):557–570. https://doi.org/10.1142/S0218488502001648

    Article  Google Scholar 

  • Wagner I, Eckhoff D (2018) Technical privacy metrics: a systematic survey. ACM Comput Surv 51(3):57:1–57:38. https://doi.org/10.1145/3168389

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Correspondence to Frank Kargl .

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Kargl, F., van der Heijden, R.W., Erb, B., Bösch, C. (2019). Privacy in Mobile Sensing. In: Baumeister, H., Montag, C. (eds) Digital Phenotyping and Mobile Sensing. Studies in Neuroscience, Psychology and Behavioral Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-31620-4_1

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