Skip to main content
SpringerLink
Log in

Privacy and Ethical Challenges in Big Data

  • Conference paper
  • First Online:
Foundations and Practice of Security (FPS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11358))

Included in the following conference series:

Abstract

The advent of Big Data coupled with the profiling of users has lead to the development of services and decision-making processes that are highly personalized, but also raise fundamental privacy and ethical issues. In particular, the absence of transparency has lead to the loss of control of individuals on the collection and use on their personal information while making it impossible for an individual to question the decision taken by the algorithm and to make it accountable for it. Nonetheless, transparency is only a prerequisite to be able to analyze the possible biases that personalized algorithms could have (e.g., discriminating against a particular group in the population) and then potentially correct them. In this position paper, I will review in a non-exhaustive manner some of the main privacy and ethical challenges associated with Big Data that have emerged in recent years before highlighting a few approaches that are currently investigated to address these challenges.

Sébastien Gambs is supported by the Canada Research Chair program as well as by a Discovery Grant and a Discovery Accelerator Supplement Grant from NSERC.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 74.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://www.d4d.orange.com/en/Accueil.

  2. 2.

    https://www.nature.com/articles/sdata2018286.

  3. 3.

    https://www.theverge.com/2012/3/1/2835250/google-unified-privacy-policy-change-take-effect.

  4. 4.

    http://www.fatml.org.

  5. 5.

    https://fatconference.org.

References

  1. Acar, G., Eubank, C., Englehardt, S., Juárez, M., Narayanan, A., Díaz, C.: The Web never forgets: persistent tracking mechanisms in the wild. In: ACM Conference on Computer and Communications Security, pp. 674–689 (2014)

    Google Scholar 

  2. Aïvodji, U., Arai, H., Fortineau, O., Gambs, S., Hara, S., Tapp, A.: Fairwashing: the risk of rationalization. CoRR abs/1901.09749 (2019)

    Google Scholar 

  3. Blondel, V.D., Decuyper, A., Krings, G.: A survey of results on mobile phone datasets analysis. EPJ Data Sci. 4(1), 10 (2015)

    Article  Google Scholar 

  4. Diakopoulos, N.: Algorithmic accountability reporting: on the investigation of black boxes. Tow Center (2014)

    Google Scholar 

  5. Doshi-Velez, F., Kim, B.: A Roadmap for a Rigorous Science of Interpretability. CoRR abs/1702.08608 (2017)

    Google Scholar 

  6. Dwork, C.: Differential privacy. ICALP 2, 1–12 (2006)

    MathSciNet  MATH  Google Scholar 

  7. Fredrikson, M., Lantz, E., Jha, S., Lin, S., Page, D., Ristenpart, T.: Privacy in pharmacogenetics: an end-to-end case study of personalized warfarin dosing. In: USENIX Security Symposium, pp. 17–32 (2014)

    Google Scholar 

  8. Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P., Roth, D.: A comparative study of fairness-enhancing interventions in machine learning. CoRR abs/1802.04422 (2018)

    Google Scholar 

  9. Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: a survey of recent developments. ACM Comput. Surv. 42(4), 14:1–14:53 (2010)

    Article  Google Scholar 

  10. Gambs, S., Killijian, M.-O., del Prado Cortez, M.N.: Show me how you move and i will tell you who you are. Trans. Data Privacy 4(2), 103–126 (2011)

    MathSciNet  Google Scholar 

  11. Goldberg, I.: Digital privacy: theory, technologies, and practices. In: Privacy-Enhancing Technologies for the Internet III: Ten Years Later, December 2007

    Google Scholar 

  12. Goldreich, O.: Foundations of Cryptography. Cambridge University Press, Cambridge (2009)

    MATH  Google Scholar 

  13. Goodman, B., Flaxman, S.R.: European Union Regulations on algorithmic decision-making and a “Right to Explanation”. AI Mag. 38(3), 50–57 (2017)

    Article  Google Scholar 

  14. Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D.: A survey of methods for explaining black box Models. ACM Comput. Surv. 51(5), 93:1–93:42 (2019)

    Google Scholar 

  15. Janic, M., Wijbenga, J.P., Veugen, T.: Transparency enhancing tools (TETs): an overview. STAST, pp. 18–25 (2013)

    Google Scholar 

  16. LeCun, Y., Bengio, Y., Hinton, G.E.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  17. Lécuyer, M., et al.: XRay: enhancing the Web’s transparency with differential correlation. In: USENIX Security Symposium, pp. 49–64 (2014)

    Google Scholar 

  18. Lindell, Y., Pinkas, B.: Secure multiparty computation for privacy-preserving data mining. IACR Cryptology ePrint Archive 2008:197 (2008)

    Google Scholar 

  19. Lou, Y., Caruana, R., Gehrke, J.: Intelligible models for classification and regression. In: KDD, pp. 150–158 (2012)

    Google Scholar 

  20. Milli, S., Schmidt, L., Dragan, A.D., Hardt, M.: Model reconstruction from model explanations. CoRR abs/1807.05185 (2018)

    Google Scholar 

  21. Naveed, M., Ayday, E., Clayton, E.W., Fellay, J., Gunter, C.A., Hubaux, J.-P., Malin, B.A., Wang, X.F.: Privacy in the Genomic Era. ACM Comput. Surv. 48(1), 6:1–6:44 (2015)

    Article  Google Scholar 

  22. Pasquale, F.: The Black Box Society, the Secret Algorithms that Control Money and Information. Harvard University Press, Cambridge (2015)

    Book  Google Scholar 

  23. Romei, A., Ruggieri, S.: Discrimination data analysis: a multi-disciplinary bibliography. In: Custers, B., Calders, T., Schermer, B., Zarsky, T. (eds.) Discrimination and Privacy in the Information Society, pp. 109–135. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-30487-3_6

    Chapter  Google Scholar 

  24. Rouvroy, A., Berns, T.: Gouvernementalité algorithmique et perspectives d’émancipation. Le disparate comme condition d’individualisation par la relation? Réseaux, n 177 (2013)

    Google Scholar 

  25. Ruggieri, S., Pedreschi, D., Turini, F.: Data mining for discrimination discovery. TKDD 4(2), 91–940 (2010)

    Article  Google Scholar 

  26. Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: IEEE Symposium on Security and Privacy, pp. 3–18 (2017)

    Google Scholar 

  27. Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(5), 557–570 (2002)

    Article  MathSciNet  Google Scholar 

  28. Verma, S., Rubin, J.: Fairness definitions explained. In: FairWare@ICSE 2018, pp. 1–7 (2018)

    Google Scholar 

  29. Zliobaite, I.: Measuring discrimination in algorithmic decision making. Data Min. Knowl. Discov. 31(4), 1060–1089 (2017)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université du Québec à Montréal (UQAM), Montreal, Canada

    Sébastien Gambs

Authors
  1. Sébastien Gambs
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sébastien Gambs .

Editor information

Editors and Affiliations

  1. Dalhousie University, Halifax, NS, Canada

    Nur Zincir-Heywood

  2. École des Mines de Nancy, Nancy, France

    Guillaume Bonfante

  3. Concordia University, Montreal, QC, Canada

    Mourad Debbabi

  4. Telecom SudParis, Evry, France

    Joaquin Garcia-Alfaro

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gambs, S. (2019). Privacy and Ethical Challenges in Big Data. In: Zincir-Heywood, N., Bonfante, G., Debbabi, M., Garcia-Alfaro, J. (eds) Foundations and Practice of Security. FPS 2018. Lecture Notes in Computer Science(), vol 11358. Springer, Cham. https://doi.org/10.1007/978-3-030-18419-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-18419-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18418-6

  • Online ISBN: 978-3-030-18419-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation