DEMOCRATIZING BIG DATA: THE ETHICAL CHALLENGES OF SOCIAL MINING. Dino PEDRESCHI (KDDLab, Dipartimento di Informatica, Università di Pisa)

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1 DEMOCRATIZING BIG DATA: THE ETHICAL CHALLENGES OF SOCIAL MINING Dino PEDRESCHI (KDDLab, Dipartimento di Informatica, Università di Pisa)

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3 Siamo tutti pollicini digitali Plenty of digital breadcrumbs behind us La Vita Nova, e- magazine de Il Sole 24 Ore Fosca Giannotti, Dino Pedreschi Dicembre 2012 Everyone is becoming a «statistical entity»

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5 Personal data as economic asset 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

6 Who s got the big benefits so far? A fewlatifundists of data The web giants Profiling for behavioral advertising and target marketing The national security agencies Profiling for discovering potential terrorists

7 Limited benefits for (most) people Limited access to knowledge for ordinary people, compared to potential uses of big data: Collective knowledge as a common good has not emerged, and Individual knowledge is not easily accessible Big data, nowadays, means Surveillance, Social control Intrusion into personal sphere Lack of transaparency (of ranking, profiling, decisions) Therefore, trust is scarse

8 Little for many, much for a few Paradoxical, because only the individual citizen has the power to link all her own digital breadcrumbs and achieve a profound self-knowledge Self-awareness But the current state of affairs is not ineluctable! We want more privacy AND more benefits from big data!

9 Democratizing 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

10 Two lines of attack for ethically responsible social mining 1. Set the analytical power of data free by privacy-by-design 2. Design a techno-social ecosystem for a new deal on personal data

11 PRIVACY-BY-DESIGN IN SOCIAL MINING

12 Privacy-preserving data mining Many technologies already available Privacy-by-design for data analytics We still lack a techno-legal framework to weigh among: The right to privacy and data protection The right to access vital societal knowledge as a common good Privacy risks vs non-privacy rewards

13 Privacy-by-Design in Data Analytics Service Provider Mining and Analytical Engine Anonymization Anonymization is not trivial De-identification is not enough

14 Privacy by Design in Data Mining In most analytic services, the dilemma between privacy protection and knowledge sharing can be fixed Make data anonymous with reference to the specific service Use anonymous data to extract knowledge Very little loss in data quality often coexist with strong privacy protection Anna Monreale. Privacy by Design in Data Mining. PhD Thesis, Univ. Pisa, 2011

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

16 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= )..

17 A NEW DEAL ON PERSONAL DATA DEMOCRATIZING BIG DATA

18 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

19 «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

20 Whendo I typicallygo to the supermarket?

21 Conoscenza individuale e conoscenza collettiva Novel Indicators Collective Knowledge Collective patterns 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 Data Mobility Traditional DataDB sources

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23 Quoting «Big data for all» Individuals must be offered meaningful rights to access their data in usable, machine-readable format. This, in turn, will unleash a wave of innovation for userside applications and services based on access to personal information, a process we refer to as the featurization of big data. Featurization will allow individuals to declare their own policies, preferences and terms of engagement, and do it in ways that can be automated both for them and for the institutions and companies they engage. O. Tene, J. Polonetsky. Big Data for All: Privacy and User Control in the Age of Analytics.2012

24 Engage citizens into the game Through self-knowledge, people can understand the value of data (and knowledge) And can decide to engage with trusted parties, answering questions rather than just giving out data, sharing part of their personal information to improve services that are perceived useful and trustable A city is smart if it is made of participating citizens

25 Participation, not surveillance If the new deal takes off, then there will be more social pressure towards transparency To expose the logic adopted for reputation, ranking, profiling, decisions Diversity, not conformity More opportunities for social and economic innovation, also for small companies and public administrations Privacy is also a fundamental driver for diversity, innovation and creativity!

26 The reform of EC data protection directive New proposed directive submitted to European Parliament on Jan 25, 2012 Topics related to the new deal on data: Data portability Right to oblivion Limits to profiling and automated decision making Privacy-by-design

27 SOBIGDATA MISSION

28 SoBigData is a lab where experiences at the frontier of big data analytics can be conducted, within this ethical context SoBigData embraces this complexity to search for innovative syntheses between societal benefits of big data and ethical values to help create a techno-social ecosystem based on participation and trust

29 We are a group of scientists from different universities and CNR research groups, and different disciplines, with a common goal towards the quantitative analysis of societies Our aims: Share competences, tools, and data Provide a platform for collaborative projects with industry and public bodies Amplify funding opportunities Provide a «data scientist» education platform to PhD s and young researchers Create dissemination events, like this one Maintain a web portal of resources

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