Successfully reported this slideshow.

Course 5: GDPR & Big Data by Sari Depreeuw

1

Share

Loading in …3
×
1 of 39
1 of 39

Course 5: GDPR & Big Data by Sari Depreeuw

1

Share

Download to read offline

For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/
---------
"Data is the new oil" - Many companies and professionals do not know how to use their data or are not aware of the added value they could gain from it.

It is in response to these problems that the project “Brussels: The Beating Heart of Big Data” was born.

This project, financed by the Region of Brussels Capital and organised by Betacowork, offers 3 training cycles of 10 courses on big data, at both beginner and advanced levels. These 3 cycles will be followed by a Hackathon weekend.

No prerequisites are required to start these courses. The aim of these courses is to familiarize participants with the principles of Big Data.
------
For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/

For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/
---------
"Data is the new oil" - Many companies and professionals do not know how to use their data or are not aware of the added value they could gain from it.

It is in response to these problems that the project “Brussels: The Beating Heart of Big Data” was born.

This project, financed by the Region of Brussels Capital and organised by Betacowork, offers 3 training cycles of 10 courses on big data, at both beginner and advanced levels. These 3 cycles will be followed by a Hackathon weekend.

No prerequisites are required to start these courses. The aim of these courses is to familiarize participants with the principles of Big Data.
------
For more info about our Big Data courses, check out our website ➡️ https://www.betacowork.com/big-data/

More Related Content

Course 5: GDPR & Big Data by Sari Depreeuw

  1. 1. Big Data and GDPR Sari Depreeuw Beta Cowork Brussels 21 March 2019 1
  2. 2. Overview • Introduction • Does GDPR apply? – « personal data » – « processing » • Processing within limits – Principles – Legal grounds • How to do big data (right)? – Impact assessment – Data protection by design & by default – Transparency – Data quality – Data subjects’ rights 2
  3. 3. Introduction 3
  4. 4. Introduction 4
  5. 5. Risks • Tracking & surveillance (government/commercial) • Security • Trust (understanding, control) • Inaccuracy • Discrimination / influencing (nudging) / unfair treatment / perpetuating existing (economic) imbalance 5
  6. 6. Opportunities • Autonomous cars • Insurance • Marketing / ads (online, offline) • Retail • Mobility • Search • Public sector / private sector 6
  7. 7. “Big data” 1. Collecting data – Large quantities of data – From diverse sources – Variety of data (personal and non-personal data) 2. Analysing data – Correlations – profiles – algorithms (black box) – “Anonymisation” 3. Applying profiles (if applicable) – Fitting individuals in profiles 7
  8. 8. Does the GDPR apply? “personal data”, “processing”, “anonymisation” v. “pseudonymisation” 8
  9. 9. Personal data • “Personal data” (art. 4(1) GDPR) – Any information relating to an identified or identifiable natural person (‘data subject’) – Large notion! • “Identifiable natural person” – Anyone who can be identified - directly or indirectly, e.g. by reference to: • identifier (e.g. name, identification number, location data, online identifier) • factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person • e.g. combination of IP address, MAC address, browser type, type of phone 9
  10. 10. Personal data (2) • Natural person “identifiable”? – Assessment on the basis of all the means reasonably likely to be used either by the controller or by any other person to identify the said person (Recital 26 of GDPR) – Factors: • costs of identification • intended purpose (identify individuals?) • advantages expected by the controller, interests at stake for the individual • future technological evolution (dynamic test)! – “Singling out” • Not necessary to know name or e-mail address • Impact on individual’s behaviour 10
  11. 11. Personal data (3) • “data relating to an individual” = data referring to the identity, characteristics or behaviour of an individual or such information that is used to determine or influence the way in which that person is treated or evaluated (WP 29) Examples: – A natural person’s name, picture, phone number (private or professional), bank account number, e-mail address. – CJEU: employees’ working time records, person’s image by CCTV, tax data, information in a press release where (unnamed) person was easily identifiable, fingerprint, IP address, exam scripts. Even if data subject acts in professional context! 11
  12. 12. Processing = Any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means (art. 4(2) GDPR) – E.g. collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction – Large interpretation – CJEU: loading on internet page; collection, publication, transfer on CD- ROM, text messaging; communication in response to a request for access to documents; crawling, indexing, transmitting by search engine; leaking to press; video recording. 12
  13. 13. Anonymisation • “Processing” of “personal data” – Collecting of data – Analysing – Applying (targeting) • Anonymisation? = act of processing Principle: no more “personal” data (rec. 26) In practice: pseudonymisation Data sets – combination – re-identification - targeting 13
  14. 14. PROCESSING WITHIN LIMITS Principles for processing, legal grounds for processing, sensitive data, profiling 14
  15. 15. Principles for processing (art. 5 GDPR) a) Lawfulness, fairness and transparency b) Purpose limitation: Data collection for specified, explicit and legitimate purposes and not further processed in a manner that is incompatible with those purposes; further processing for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes shall not be considered to be incompatible with the initial purposes c) Data minimisation: adequate, relevant and limited to what is necessary in relation to the purposes for which they are processed d) Accuracy: accurate and, where necessary, kept up to date; every reasonable step must be taken to ensure that personal data that are inaccurate, having regard to the purposes for which they are processed, are erased or rectified without delay 15
  16. 16. Principles for processing (2) e) Storage limitation: kept in a form which permits identification of data subjects for no longer than is necessary for the purposes for which the personal data are processed; storage for longer periods if processing solely for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes subject to safeguards; f) Integrity and confidentiality: processed in a manner that ensures appropriate security of the personal data, including protection against unauthorised or unlawful processing and against accidental loss, destruction or damage, using appropriate technical or organisational measures. g) Accountability: Controller is responsible for compliance with these principles and should be able to demonstrate such compliance. 16
  17. 17. Bottlenecks • Transparency >< black box, opacity • Purpose limitation >< discovery, unpredictability >< repurposing data • Data minimisation >< collecting all available data >< generating new data provided >< observed, derived, inferred 17
  18. 18. Lawfulness (art. 6 GDPR) • Legal grounds for processing : – Consent – Performance of contract – Legal obligation of controller – Vital interests of data subject or other – Public interest or official authority of controller – Legitimate interests of controller or other • Unless overriding interest or fundamental rights and freedoms of data subject (esp. child) 18
  19. 19. Consent (art. 7 GDPR) • Freely given, specific, informed, unambiguous (art. 4(11) GDPR) – Statement or affirmative action >< pre-ticked box – Concerning all purposes (initial + further processing) Set prior to processing with consent – Intelligible // public – Using clear and plain language  “legalese” – Otherwise: not binding – Valid consent = illusion? • Withdrawal of consent – At any time – As easy as granting consent – Without detriment 19
  20. 20. Bottlenecks • Consent (informed, specific) >< unpredictable outcome >< limited understanding (technophobia) Granular, real time consent? • Performance of contract >< necessity • Legal obligation >< limited cases • Legitimate interests >< necessity >< privacy, non-discrimination (cf. Google Spain) risk: balance in favour of data subject 20
  21. 21. Sensitive data (art. 9 GDPR) • Special protection for « special categories » of data Data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership Genetic data, biometric data for the purpose of uniquely identifying a natural person, data concerning health or data concerning a natural person's sex life or sexual orientation 21
  22. 22. Sensitive data (2) • Principle: prohibition • Exceptions: – Explicit consent – Rights re employment, social security, social protection law – Vital interests – Not for profit organisation with political, philosophical, religious or trade union aim – Prior publicity by data subject – Legal claims – Substantial public interest, public health – Health care related processing – Archiving, research, statistics 22
  23. 23. Profiling (art. 22 GDPR) • To be subject to (i) a decision (ii) based solely on automated processing, including profiling, (iii) which produces legal effects concerning him or her or similarly significantly affects him or her • 'profiling' means “any form of automated processing of personal data consisting of using those data to evaluate certain personal aspects relating to a natural person, in particular to analyse or predict aspects concerning that natural person's performance at work, economic situation, health, personal preferences, interests, reliability, behaviour, location or movements” (rec. 71) 23
  24. 24. Profiling (2) • Principle: data subject’s right not to be subject to automated decision Profiling as such not prohibited. • Except: – Necessary for contract (conclusion / performance) – Authorised by law – Explicit consent Safeguards: rights, freedoms, interests of data subject minimum: human intervention + defence, right to contest • No decisions based on sensitive data => prohibited discrimination Unless explicit consent or public interest + safeguards  Art. 29 WP Guidelines on Automated individual decision-making and Profiling for the purposes of GDPR (WP251) v. 6 Feb 18 24
  25. 25. HOW TO DO BIG DATA (RIGHT)? Impact assessment, data protection by design, Transparency, information, consent, 25
  26. 26. Starting point • Assume personal data is processed – Collecting => source(s)? – Analysing => statistics? – Applying => automated? Significant impact on data subject? • Define purposes (initial + further processing) Apply data processing principles (bottlenecks) • Define legal basis – Legitimate interest – Consent 26
  27. 27. Impact assessment (art. 35) • Data protection impact assessment – // type of processing (new technologies) – // nature, scope, context and purposes of the processing  Potential “high risk” to the rights and freedoms of natural persons • Required: – a systematic and extensive evaluation of personal aspects relating to natural persons, based on automated processing, including profiling, and on which decisions are based that produce legal effects concerning the natural person or similarly significantly affect the natural person; – processing on a large scale of sensitive data / data relating to criminal convictions and offences; or – a systematic monitoring of a publicly accessible area on a large scale. 27
  28. 28. Impact assessment (2) • DPIA: – Description of processing operations, purposes, legitimate interest of controller; – Assessment of necessity and proportionality of processing// purposes; – Assessment of risks to the rights and freedoms of data subjects; – the measures envisaged to address the risks e.g. https://www.cnil.fr/en/privacy-impact-assessment-pia 28
  29. 29. DP by design • Data protection by design and by default (art. 25 GDPR) – By design: • Data protection incorporated “in the design” of the solution – // state of the art technology, cost of implementation, scope, context and purposes of processing, risk for rights of data subject – “Appropriate” technical and organisational measures (e.g. pseudonymisation, data minimisation, data segregation) • Entire lifecycle management of personal data • E.g. security measures // risk Anonymisation (cf. art. 29 working party - Opinion 05/2014) Pseudonymisation & encryption, access control, audit logs, guarantee ongoing confidentiality, integrity, availability, resilience of system, restoring availability and access to data in case of physical or technical accident, regular testing and assessing security measures • E.g. functional separation (statistics X impact on individual) 29
  30. 30. DP by default – By default: • As a starting point: only data necessary per specific purpose (limitation re amount of data collected, extent of processing, storage period and accessibility) – “Appropriate” technical and organisational measures – No accessibility to indefinite number of people without intervention of the individual – E.g. control data collected through form (fields), processes to manage duration of storage (alerts, automated deletion or pseudonymisation) – To be documented! 30
  31. 31. Fairness, transparency • Impact of processing e.g. ads v. different treatment Prohibited discrimination (e.g. ethnicity, gender, religion) • Legitimate expectations e.g. loyalty card; social media (cf. Cambridge Analytica) Transparency, prior information Evolving attitude of data subjects (generational?) • Information About existence of processing (tracking?), methods cf. Facebook decisions (Brussels court of appeal) 31
  32. 32. Data quality • Accurate, up to date data cf. right to rectification • Algorithmic accountability logic – discrimination (perpetuating) – active detection (algorithm, data sets) inaccurate predictions – associations – correlation / causation 32
  33. 33. Rights of data subject (1) • Information + access (art. 13-15 GDPR) – Who (controller), why (purpose), what (data, processing, source), how (recipients, access, rectification, profiling), how long (retention period), remedies (SA, DPO) – Existence of automated decision making, incl. profiling: logic, significance, consequences for data subject – Right to obtain a copy including observed, inferred data. importance of proper data management! Give access to profiles, labels? – Information in plain language – intelligible (public) Legal design: visualisation tools? Icons? Simple text? – How to inform data subject where data from various sources? 33
  34. 34. Rights of data subject (2) • Rectification and erasure (art. 16-17 GDPR) inaccurate data => rectification Erasure => “right to be forgotten”! – Limited cases incl. withdrawal of consent, data no longer necessary,… Cf. also CJEU Google Spain (C-131/12) • Right to object (art. 21 GDPR) – To processing (and profiling) on public interest/legitimate interest ground • If particular situation • Controller may establish compelling legitimate grounds – To direct marketing (including profiling) – To processing for scientific/historical research/statistical purposes 34
  35. 35. Rights of data subject (3) • Restriction of processing (art. 18 GDPR) • Data portability (art. 20 GDPR) • “Automated individual decision making”, incl. profiling (art. 22 GDPR) – Right not to be subject to decision based solely on automated processing if legal effects or significant impact 35
  36. 36. Automated decisions • Right of information existence + logic >< IP, trade secrets • Right of access (incl. copy) to personal data incl. generated data (application of profile) existence + logic >< IP, trade secrets • Right of rectification inaccurate profiling? • Right to erasure Withdrawal of consent • Right to object Legitimate interest (incl. profiling) data subject > controller (i) particular situation, or (ii) direct marketing and profiling • Right not to be subject to automated decision making 36
  37. 37. Moving target • Anonymise where possible – Pseudonomyse is valid plan B • Data protection by design – Think about design – all the time – Start by impact assessment – Integrate GDPR + « ethical » principles – Check algorithms + data sets • Communicate transparently – Privacy notices • Document choices 37
  38. 38. Moving target (2) • Opportunities? • Algorithmic transparency (accountability) Diverse development or testing teams? Audits? Information? • Innovative communication (legal design) Bite-size, evolving messages Plain text Visualisation tools? Icons? Access to profile, applied labels? • Consent Granular consent // information? 38
  39. 39. Many thanks! Sari Depreeuw sdp@daldewolf.com 39

×