Abstract
In this chapter, we discuss the privacy implications of mobile sensing used in 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 was introduced, 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 number of examples and also highlight 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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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
Boronow KE, Perovich LJ, Sweeney L, Yoo JS, Rudel RA, Brown P, Brody JG (2020) Privacy risks of sharing data from environmental health studies. Environ Health Perspect 128(1):017008
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
Gymrek M, McGuire AL, Golan D, Halperin E, Erlich Y (2013) Identifying personal genomes by surname inference. Science 339(6117):321–324
Hoepman JH (2014) Privacy design strategies. In: IFIP international information security conference, Springer, pp 446–459
Homer N, Szelinger S, Redman M, Duggan D, Tembe W, Muehling J, Pearson JV, Stephan DA, Nelson SF, Craig DW (2008) Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays. PLoS Genet 4(8):e1000167
Joinson A (1999) Social desirability, anonymity, and internet-based questionnaires. Behav Res Methods, Inst Comput 31(3):433–438
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
Kelman HC (1977) Privacy and research with human beings. J Soc Iss 33(3):169–195
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
Lindell Y, Pinkas B (2002) Privacy preserving data mining. J Cryptol 15(3)
Meißner D, Engelmann F, Kargl F, Erb B (2021) PeQES: A platform for privacy-enhanced quantitative empirical studies. In: Proceedings of the 36th Annual ACM Symposium on Applied Computing, Association for Computing Machinery, New York, NY, USA, pp 1226–1234. https://doi.org/10.1145/3412841.3441997
Narayanan A, Shmatikov V (2008) Robust de-anonymization of large sparse datasets. 2008 IEEE Symposium on Security and Privacy (S&P 2008), 18–21 May 2008. USA, IEEE Computer Society, Oakland, California, pp 111–125
Narayanan A, Shmatikov V (2009) De-anonymizing social networks. 30th IEEE Symposium on Security and Privacy (S&P 2009), 17–20 May 2009. USA, IEEE Computer Society, Oakland, California, pp 173–187
Narayanan A, Shmatikov V (2010) Myths and fallacies of and personally identifiable information. Commun ACM 53(6):24–26
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
Rocher L, Hendrickx JM, De Montjoye YA (2019) Estimating the success of re-identifications in incomplete datasets using generative models. Nature commun 10(1):1–9
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
Serenko N, Fan L (2013) Patients’ perceptions of privacy and their outcomes in healthcare. Int J Behav Healthcare Res 4(2):101–122
Sweeney L (2002) k-anonymity: a model for protecting privacy. Int J Uncertainty, Fuzziness Knowl-Based Syst 10(5):557–570. https://doi.org/10.1142/S0218488502001648
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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Kargl, F., van der Heijden, R.W., Erb, B., Bösch, C. (2023). Privacy in Mobile Sensing. In: Montag, C., Baumeister, H. (eds) Digital Phenotyping and Mobile Sensing. Studies in Neuroscience, Psychology and Behavioral Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-98546-2_2
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