Big Data & Privacy. It s Time for a New Deal on Personal Data Dino Pedreschi. KDD LAB ISTI CNR and Univ. of Pisa

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1 Big Data & Privacy It s Time for a New Deal on Personal Data Dino Pedreschi KDD LAB ISTI CNR and Univ. of Pisa Taiwan-Italy Workshop Roma 27 Feb 2015

2 SIAMO TUTTI POLLICINI DIGITALI La Vita Nova, e-magazine de Il Sole 24 Ore Fosca Giannotti, Dino Pedreschi

3 Big data proxies of social life Shopping patterns & lifestyle Relationships & social ties Movements Desires, opinions, sentiments

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7 9 Analisi di Reti Sociali. Aprile-Maggio 2011 marzo 26, 2015

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11 CITY USERS SOCIOMETER

12 Dimmi come chiami ti dirò chi sei! Residente A Visitore B Pendolare A B A

13 City User Sociometer Chiamate Pendolari userid CellStar tid PI010U 2 PI002U 2 PI002U 2 PI034U 1 PI023U 1 CellEnd ID PI010U 2 PI002U 2 PI002U 2 PI034U 1 PI023U 1 TimestampStart :37: :46: :53: :59: :03:21+01 TimestampEnd :38: :46: :53: :02: :04:56+01 Analisi di data mining Anonimizzazione Visitatori/Turisti Profilo aggregato Mappa dei profili Residenti

14 Urban Sociometer: the city user indicator Analysing the GSM call habits we can build continuous indicators of social profiles Pisa january 2012

15 Big Data

16 big data bene comune

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18 DEMOCRATIZING BIG DATA: THE ETHICAL CHALLENGES OF SOCIAL MINING

19 Big Data Analytics & Social Mining The Social Microscope

20 a tool to measure, understand, and possibly predict human behavior

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23 Personal data is the new oil of the Internet and the new currency of the digital world Maglena Kuneva, former European Commissioner of Consumer Protection

24 Who s got the big benefits so far? A few latifundists of data GAFA Profiling for behavioral advertising and target marketing NSA Profiling for discovering potential terrorists Mass surveillance STASI_2.0

25 THE DARK SIDE OF BIG DATA

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29 Quale è il limite al controllo sociale?

30 BIG BROTHER BIG DATA COMMONS

31 BIG DATA, EUROPEAN WAY PRIVACY-BY-DESIGN NEW DEAL ON DATA

32 Privacy by Design in Data Mining In many cases (most analy7c ques7ons), it is possible to reconcile the dilemma between privacy protec7on and knowledge sharing Make data anonymous with reference to a specific service Use anonymous data to extract knowledge Only a lible loss in data quality ocen earns a strong privacy protec7on Anna Monreale : Privacy by Design in Data Mining. PhD Thesis 2011

33 Privacy-by-Design in Data Analytics Service Provider Mining and AnalyCcal Engine AnonymizaCon AnonymizaCon is not trivial De- idencficacon is not enough

34 Privacy-Aware socio-meter Profiles computed on anonymous data Output: quan7fica7on of profiles (safe!!) Clustering for Building profiles Aggregated call ac7vi7es assuring anonymity computed by the Telco Operator User 1 User 2.. User n- 1 User n A. Monreale, F. GiannoP, D. Pedreschi, S. Rinzivillo. Privacy- by- design for big data analy7cs. 2013

35 Probability of re-identification for 4 weeks (10M users) Observe the User for 4 weeks Probability of re-identification 1% users P <=0,0001(K=10000) 3% users P <= (K=300000) 70% users P <= (K= )..

36 A data-centric methodology for empirical risk assessment Ongoing project KDD LAB ISTI CNR Pisa Toyota InfoTechnology Center Tokyo Anirban Basu, Anna Monreale, Juan Camilo Corena, Fosca Giannotti, Dino Pedreschi, Shinsaku Kiyomoto, Yutaka Miyake, Tadashi Yanagihara, Roberto Trasarti. A Privacy Risk Model for Trajectory Data. Trust Management VIII - 8th IFIP WG Int. Conf., IFIPTM Proceedings, p , Springer 2014

37 Set the power of big data free 2013/12/0 9 Providing data from business to other business partners. Data used in many ways (Research/Commercial). The required privacy level differs depending on the user. Example: Usage of Probe Data Probe Database Data Center User of Data In-house Departments Group Businesses Parastatal Facilities Required Privacy Level LOW Probe Data Probe Data Probe Data Research Facilities Third Parties HIGH Necessity of method to allow free distribution of (derived) data to various users. 39

38 Privacy-by-Design in Big Data Analytics For each query: - Attack Models - Privacy Measures In case of privacy risks - Privacy by design data transformation

39 RaC Risk and Coverage Curve

40 Exploring the space of RaC curves DB (Raw data) Dimensions from Data (giving rise to different mappings f) Dimensions from Background Knowledge BK Risk Assessment Best Configuration Risk Mitigation: Randomization Generalization..

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42 Data Statistics Use case 5 Area Covered: 4611 Km 2 Time slots for the analysis 1 hour. Distribution of users moving in the different time slots: Number of trajectories: Number of users: Temporal window: 1 month Note: given two location L1 and L2 and the travel time t needed to reach L2 from L1, considering an average speed of 70 Km/h, t is <= 1 hour.

43 Attack Model 5.1: RAC U

44 i-rac Indexes

45 A NEW DEAL ON PERSONAL DATA DEMOCRATIZING BIG DATA

46 A user-centric ecosystem for Big Data Engage people in the creation and use of big data and knowledge, by empowering individuals with self-knowledge Incentivize individual to participate, to align own self-interest with broader societal goals Based on transparency, trust & privacy

47 Democratize Big Data Design a user-centric ecosystem for big data Empower everyone with tools to integrate own digital breadcrumbs into meaningful knowledge, to boost self-awareness Conosci te stesso Know yourself

48 «Know yourself» examples Your own health records Not only for improving health care system But also for understanding more about your health status, nutrition behavior, prevention advises,..., compared to others Your mobility tracks Not only for insurance control, traffic monitoring But also for understanding your behavior, compared to others

49 When do I typically go to the supermarket?

50 Conoscenza individuale e conoscenza collettiva Novel Indicators Collective Knowledge Collec7ve paberns Individual profiles Personal data store for self knowledge Conoscenza di se Self- awareness Social mining of many individuals Social mining of individual histories Mobility Data Mobility Data Mobility Data Mobility Data Mobility Data Mobility Traditional Data DB Mobility sources Data

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52 Toscana Mobile Territorial Lab HII project Trusted Cloud of EIT-ICT LAB A living lab of volunteer users who will create a Personal Data Store ecosystem Focus on mobility, telecom services and supermarket transactions Joint project of

53 Key publications F Giannotti, M Nanni, F Pinelli, D Pedreschi. Trajectory pattern mining. ACM SIGKDD 2007 F Giannotti, D Pedreschi. Mobility, data mining and privacy: Geographic knowledge discovery. Springer, 2008 A Monreale, F Pinelli, R Trasarti, F Giannotti. WhereNext: a location predictor on trajectory pattern mining. ACM SIGKDD 2009 S Rinzivillo, D Pedreschi, M Nanni, F Giannotti, N Andrienko, G Andrienko. Visually driven analysis of movement data by progressive clustering. Information Visualization 7 (3-4), D Wang, D Pedreschi, C Song, F Giannotti, AL Barabasi. Human mobility, social ties, and link prediction. ACM SIGKDD 2011 F Giannotti, M Nanni, D Pedreschi, F Pinelli, C Renso, S Rinzivillo, R Trasarti. Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB 20(5) 2011 R Trasarti, F Pinelli, M Nanni, F Giannotti. Mining mobility user profiles for car pooling. ACM SIGKDD 2011

54 Key publications M Coscia, G Rossetti, F Giannotti, D Pedreschi. Demon: a local-first discovery method for overlapping communities. ACM SIGKDD 2012 S Rinzivillo, S Mainardi, F Pezzoni, M Coscia, D Pedreschi, F Giannotti. Discovering the geographical borders of human mobility. KI- Künstliche Intelligenz 26 (3) 2012 D Pennacchioli, M Coscia, S Rinzivillo, D Pedreschi, F Giannotti. Explaining the Product Range Effect in Purchase Data. IEEE BIGDATA 2013 B Furletti, L Gabrielli, C Renso, S Rinzivillo. Analysis of GSM Calls Data for Understanding User Mobility Behavior. IEEE BIG DATA 2013 L Milli, A Monreale, G Rossetti, D Pedreschi, F Giannotti, F Sebastiani. Quantification trees. IEEE ICDM 2013

55 Vision papers F GiannoP, D Pedreschi, A Pentland, P Lukowicz, D Kossmann, J Crowley, D Helbing. A planetary nervous system for social mining and colleccve awareness. The European Physical Journal Special Topics 214 (1), 49-75, 2012 J van den Hoven, D Helbing, D Pedreschi, J Domingo- Ferrer, F GiannoP. FuturICT The road towards ethical ICT. The European Physical Journal Special Topics 214 (1), , 2012 M BaBy, KW Axhausen, F GiannoP, A Pozdnoukhov, A Bazzani, M Wachowicz. Smart cices of the future. The European Physical Journal Special Topics 214 (1), , 2012

56 Special thanks to Fosca GiannoP, co- lead of KDD LAB Salvo Rinzivillo, Mirco Nanni, Roberto Trasar7, Salvatore Ruggieri, Chiara Renso, Anna Monreale, Franco Turini all the fantas7c folks at KDD LAB Pisa many interna7onal collaborators

57 Knowledge Discovery & Data Mining Laboratory kdd.isti.cnr.it

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