Big but personal data - South Asia Institute
Transcription
Big but personal data - South Asia Institute
Big but personal data How our behavior makes us unique Yves-Alexandre de Montjoye MIT Media Lab 12 points Is the way you move as unique as your fingerprint We can use points to identify a fingerprint Scott 1 point for mobility data From 10 to 11am ~ 1 km² 2 points Around 11:30am 3 points For lunch Boston How many points do I need to uniquely identify a mobility traces? De-identification Entire country of 1.5 millions people Our behavior is unique enough 4 points Identify 95% of people Resolution: 800 pixels Resolution: 300 pixels Resolution: 150 pixels Resolution: 75 pixels Resolution: 30 pixels Where’s Thierry ? ? 11am noon 8am ––12pm Estimating Privacy Number of points Spatial resolution Temporal resolution Harder to find people Harder to find people Much easier to find people It’s hard to hide in the crowd de Montjoye, Y. A., Hidalgo, C. A., Verleysen, M., & Blondel, V. D. (2013). Unique in the Crowd: The privacy bounds of human mobility. Nature SRep, 3. BFI: Personality test BFI: Personality test Behavioral indicators derived from metadata using the Bandicoot toolbox Predicting personality using metadata de Montjoye, Y. A., Quoidbach, J., Robic, F., & Pentland, A. S. (2013). Predicting personality using novel mobile phone-based metrics. In Social Computing, Behavioral-Cultural Modeling and Prediction (pp. 48-55). Springer Berlin Heidelberg. Unicity: quantifying the privacy-utility trade-off Simple anonymization does not work even when the data is coarse 1. Informed anonymization 2. Online system Informed anonymization: D4D Challenge e.g. 2-week mobility traces of 27 x 300.000 individuals + Bandicoot indicators de Montjoye, Y. A., Smoreda, Z., Trinquart, R., Ziemlicki, C., & Blondel, V. D. (2014). D4D-Senegal: The Second Mobile Phone Data for Development Challenge. arXiv preprint arXiv:1407.4885. Online systems: from privacy to security using openPDS - Only shares answers, not raw data - Individual quantification of the risks openPDS de Montjoye Y.-A., Wang S., Pentland A., On the Trusted Use of LargeScale Personal Data. IEEE Data Engineering Bulletin, 35-4 (2012). de Montjoye, Y. A., Shmueli, E., Wang, S. S., & Pentland, A. S. (2014). openPDS: Protecting the Privacy of Metadata through SafeAnswers. PLoS ONE, 9(7), e98790. Yves-Alexandre de Montjoye MIT Media Lab [email protected] http://deMontjoye.com In collaboration with Alex “Sandy” Pentland, César Hidalgo, Vincent Blondel, Michel Verleysen, Erez Shmueli, Arek Stopczynski, Sune Lehmann