Privacy & data protection in big data: Fact or Fiction?

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1 Privacy & data protection in big data: Fact or Fiction? Athena Bourka ENISA ISACA Athens Conference European Union Agency for Network and Information Security

2 Agenda 1 Privacy challenges in big data: what is new today? 2 The EU legal framework on data protection 3 Privacy by design in big data 4 Privacy Enhancing Technologies in big data 5 Conclusions 2

3 Understanding big data Volume: Huge amounts of data in the scale of zettabytes and more. Velocity: Real time streams of data flowing from diverse resources (e.g. physical sensors or virtual sensors coming from social media, such as Twitter streams). Variety: Data from a vast range of systems and sensors, in different formats and datatypes. Veracity: Incompleteness (inconsistency, inaccuracy) of data. Big data include personal data: e.g. a name, a picture, contact details, posts on social networking websites, healthcare data, location data or a computer IP address. 3

4 Big data analytics Data Acquisition/Collection: gathering, filtering and cleaning data from a variety of sources (e.g. social networks, mobile apps, wearable devices, smart grids, online retail services, public registers, etc.) Data Analysis: making the «raw» collected data amenable for decision-making and usage - combination of data from different sources in order to derive new information. Data Curation: ensuring that data meets the necessary quality requirements for effective usage - assuring data reusability. Data Storage: storing and managing data in a scalable way satisfying the needs of applications/analytics that require access to the data. Data Usage: the use of the data by interested parties, such as banks, retailers, advertising networks, public authorities, etc. 4

5 Personal data processing: examples 5

6 Privacy & big data (1) Its all about the scale! Lack of control and transparency: data collection is based on so many different and unexpected sources that control for the individual can easily be lost (no information or choice). Examples: data captured from sensors and cameras, screening posts in social networks or analysis of web searches. Data reusability: using data, alone or in combination with other datasets, beyond its original point and scope of collection. Example: reusing data of mobile apps providers for advertising. 6

7 Privacy & big data (2) Data inference and re-identification: linking data and deriving personal data from «anonymous» information. Widely known personal data breaches: AOL (2006), Netflix (2007), Target (2012). Profiling and automated decision making: the filter bubbles effect offering/excluding from services based on specific profiles (isolation, discrimination, price differentiation). Examples: online behavioural advertising, predictive police algorithms. Difficulty to monitor and enforce privacy requirements & obligations! 7

8 The EU data protection legal framework (1) Directive 95/46/EC and Directive 2002/58/EC (e-privacy). Personal data: any information relating to an identified or identifiable natural person (data subject). Focus on indirect identification for big data (singling out). Anonymous data: falls outside the EU legal framework if the individuals can no longer be identified ( all the means likely reasonably to be used ). Data controller: determines the purposes and the means of the processing of personal data and has specific obligations. 8

9 The EU data protection legal framework (2) Key principles & concepts: Fairness (lawful collection & processing of data) - Consent as the legal basis for many big data cases. Purpose limitation (only for specific defined purpose) Data minimisation: only the data absolutely necessary for the defined purpose. Transparency: proper information and access rights to the data subjects. Security of the processing. 9

10 The proposed General Data Protection Regulation (GDPR) Refines Directive 95/46/EC in order to cope with the new technological context. Increased transparency Data portability Right to erasure (right-to-be-forgotten) Data Protection Impact Assessment Privacy by design & by default Co-controlleship & sharing of damages Increased enforcement powers and fines (Data Protection Authorities) 10

11 Is big data the end of privacy? Big data and the EU data protection principles seem fundamentally opposing. Data re-use against purpose limitation Massive data collection against data minimization Unlimited and secret data collection against transparency & control Profiling against free and independent choice «No matter how many times a privileged straight white male technology executive «People pronounces have really the gotten death comfortable of privacy, Privacy not only Is sharing Not Dead. more People of all «If information you ages have care something and deeply different about that kinds, privacy. you don t but And more want they openly anyone care just and to as know, with much more maybe about people. you privacy That «You social have shouldn t norm zero is privacy just be online doing something anyway. as it they Get the that do over first offline.» has place.» it!» evolved over time.» Scott Danah McNealy, Mark Boyd, Eric Sun Zuckerberg, Schmidt, social Microsystems media Google Facebook scholar & researcher 11

12 From «big data versus privacy» to «big data with privacy» The oxymoron of big data and privacy. lack of privacy resulting to low data quality (commoditization of personal data) individuals tend to «correct» privacy intrusion Privacy building trust in big data for the benefit of both users and services providers. Technology for big data privacy. 12

13 Privacy and data protection by design Embedding privacy measures and privacy enhancing technologies (PETs) directly into the design of data processing systems. PRIVACY BY DESIGN STRATEGY Minimize Hide Separate Aggregate Inform Control Enforce Demonstrate DESCRIPTION Personal data should be restricted to the minimal amount possible. Personal data and their interrelations should be hidden from plain view. Personal data should be processed in a distributed fashion and separate compartments (whenever possible). Personal data should be processed at the highest level of aggregation and with the least possible detail in which it is (still) useful. Data subjects should be adequately informed (transparency). Data subjects should be provided agency over processing of their data. A privacy policy compatible with legal requirements should be in place and should be enforced. Data controllers should be able demonstrate compliance with privacy policy into force and any applicable legal requirements. ENISA s 2014 report on privacy & data protection by design (available online) 13

14 Privacy by design in big data BIG DATA VALUE CHAIN Data collection Data analysis & data curation KEY PRIVACY BY DESIGN STRATEGY MINIMIZE AGRREGATE HIDE INFORM CONTROL AGRREGATE HIDE IMPLEMENTATION Select before collect (reduce data fields, define relevant controls, delete unwanted information), Privacy Impact Assessments. Local anonymisation (at source). Privacy enhancing end-user tools, e.g. anti-tracking tools, encryption tools, identity masking tools, secure file sharing. Appropriate notice & transparency mechanisms. Consent & opt-out mechanisms. Mechanisms for expressing privacy preferences, sticky policies, personal data stores. Anonymisation (k-anonymity family, Differential privacy). Searchable encryption, privacy preserving computations. Encryption of data at rest. Data storage HIDE Authentication and access control mechanisms. Other measures for secure data storage. SEPARATE Distributed storage and analytics facilities. Data use AGRREGATE Anoymisation techniques. Data quality, data provenance. 14

15 Privacy enhancing technologies in big data (1) Anonymisation: the traditional approach to big data analytics. New re-identification problems in big data. The trade off between privacy and utility. Review of privacy models: k-anonymity versus differencial privacy («release and forget» versus question/answers systems). Particular issues for big data: streaming data, large volumes, decentralised (local) anonymization. Need for new privacy models & methods new ways of calculating the data inference risk. Not a «fit for all» solution implies compliance with other privacy requirements. 15

16 Privacy enhancing technologies in big data (2) Encryption: going beyond the traditional «encrypt all or nothing» model. Attribute Based Encryption, Identity Based Encryption, Functional Encryption. Encrypted search: trade offs between privacy, efficiency and query expressiveness. Property Preserving Encryption Structured Encryption (Symmetric Searchable Encryption, Public Key Searchable Encryption) Homomorphic Encryption Oblivious RAM Secure Multiparty Computation Emerging research field need for practical implementations. 16

17 Privacy enhancing technologies in big data (3) Privacy by security: a coherent security framework for the protection of personal data. Granular access control: methods enabling finer grained access control policies. Attribute Based Credentials (ABCs). Logging and accountability. Enforcement of persistent privacy policies. Data provenance: avoiding personal data inference from metadata. 17

18 Privacy enhancing technologies in big data (4) Transparency mechanisms: beyond the long and legalistic privacy policies Layered notices. Privacy icons (e.g. Mozilla, Disconnect). Privacy enhancing tools for end-users (e.g. showing tracking behaviour). Automated access mechanisms & data portability tools. 18

19 Privacy enhancing technologies in big data (5) User control: mechanisms for empowering the end user. New practical ways of consent (including visual representations). Privacy preferences & sticky policies. Cryptophically-based policy enforcement. Personal data stores. 19

20 Conclusions 01 Privacy by design applied: need for guidance, examples and support Centralised versus decentralised data analytics: selectiveness (for effectiveness) in the age of big data. Policy enforcement: making privacy policies persistent and automating their enforcement in big data. Transparency & control: giving the users the knowledge and tools to recover trust. 05 Beyond PETs: legal obligations & responsibilities of involved parties. 20

21 «In an emerging big data world, privacy can be the touch of wisdom for a more humanistic development & use of technology.» 21

22 Thank you PO Box 1309, Heraklion, Greece Tel:

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