Privacy Issues and Data Protection in Technology Enhanced Learning - - PowerPoint PPT Presentation

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Privacy Issues and Data Protection in Technology Enhanced Learning - - PowerPoint PPT Presentation

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Privacy Issues and Data Protection in Technology Enhanced Learning Seda Grses COSIC, K.U. Leuven dataTEL 2011 Alpines Rendez-vous Thursday, March 31, 2011 1 - mendeley: - group: privacy and dataTEL - slides: - after talk: -

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Privacy Issues and Data Protection in Technology Enhanced Learning

Seda Gürses COSIC, K.U. Leuven dataTEL 2011 Alpines Rendez-vous

1 Thursday, March 31, 2011

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  • mendeley:
  • group: privacy and dataTEL
  • slides:
  • after talk:
  • http://www.esat.kuleuven.be/~sguerses/

talks.html

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2 Thursday, March 31, 2011

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  • utline
  • introduction to privacy notions
  • building systems that address privacy

concerns

  • privacy solutions
  • privacy and ethics
  • conclusion

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privacy?

  • what is privacy?
  • what are privacy requirements?
  • in security engineering: confidentiality

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  • nline social networks (SNS)

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  • nline social networks

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privacy data protection

non-absolute relational contextual

  • pacity of the individual

procedural safeguards

accountability

transparency personal data

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sousveillance

surveillance

dataveillance

covaillance

cumulative disadvantage

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data protection

notice and consent

purpose and proportionality

subject acces rights data disclosure

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  • how do we deal with these issues when

developing systems?

  • specifically: during requirements engineering

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multilateral privacy requirements engineering

  • reconcile:
  • privacy notions (legal & surveillance studies)
  • privacy solutions (computer science)
  • in a social context (dataTEL contexts)
  • multilaterally
  • during requirements engineering

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privacy requirements definition

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lack of universality lack of satisfiability subjectivity legal compliance contrivability environmental factors counter - factuality temporality agonism negotiability

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multilateral privacy requirements engineering

  • reconcile:
  • privacy notions (legal & surveillance studies)
  • privacy solutions (computer science)
  • in a social context (dataTEL contexts)
  • multilaterally
  • during requirements engineering

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solutions from privacy research

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data confidentiality anonymous communications PPDM/PPDP IDMS Differential Privacy Privacy Policy Languages Feedback and Awareness Systems

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privacy research paradigms

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privacy as confidentiality the right to be let alone.

Warren & Brandeis (1890) hiding information and identity

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privacy research paradigms

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privacy as confidentiality the right to be let alone.

Warren & Brandeis (1890) hiding information and identity

privacy as control

separation of identities, data protection principles right of the individual to decide what information about himself should be communicated to

  • thers and under what
  • circumstances. (Westin 1970)

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privacy research paradigms

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privacy as confidentiality the right to be let alone.

Warren & Brandeis (1890) hiding information and identity

privacy as control

separation of identities, data protection principles right of the individual to decide what information about himself should be communicated to

  • thers and under what
  • circumstances. (Westin 1970)

privacy as practice

the freedom from unreasonable constraints on the construction of

  • ne’s own identity (Agre, 1999)

transparency and feedback

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privacy research paradigms

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privacy as confidentiality

hiding information and identity

privacy as control

separation of identities, data protection principles

privacy as practice

transparency and feedback

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privacy as confidentiality

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main concerns

  • centralized databases
  • do not provide identifiable information
  • provide as little information as possible
  • minimize collection of any data
  • minimize data used for processing
  • control
  • hard security
  • nly communication partner receives information
  • if at all: trust and risks are minimized

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Anonymizers

(main concept)

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Anonymizers

(the model)

  • observer (adversary)
  • does not know who is communicating with whom
  • probabilistic models
  • varying degrees of anonymity:
  • entropy base metrics
  • users traces delinked from identity

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DB ANONYMIZATION

  • PPDP - PPDM
  • basic idea:
  • in the database individuals no longer

uniquely identifiable

  • keep the utility of the data
  • economic / dp approach
  • k-anonymity

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privacy as confidentiality

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anonymization fail!

  • Narayanan and Shmatikov (2010) show that:
  • you can always link disparate information

sources and identify individuals

  • so, what’s with personal data?
  • and with data protection?
  • differential privacy...
  • very theoretical interactive privacy preserving

querying system

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dataTEL and confidentiality

  • if you want anonymous access to your

systems

  • anonymous communications
  • future research: usability issues
  • if you want anonymization
  • check out anonymization methods
  • interesting for dp compliance (only)

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dataTEL and confidentiality

  • if you want controlled access to your

dataset

  • future research: differential privacy model
  • e.g., Dwork 2009
  • secure multi party computation
  • e.g., Erkin et al. (2011)
  • encrypt user ratings
  • process them under encryption

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privacy as practice?

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  • make data practices transparent
  • allow users to individually and collectively

affect the flows of information

  • privacy is a social decision
  • trade off model misleading
  • including your user models?
  • research methods?

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  • feedback and awareness
  • social translucence
  • Erickson and Kellogg 2003
  • identity mirror, privacy mirror
  • individual transparency not enough
  • what can we say about the observed

population/groups?

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  • attacks on machine learning algorithms
  • Barreno et al. 2008
  • Dutrisac and Skillicorn 2008

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privacy as practice?

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  • users may want to be open to negotiating

data collection, processing, distribution

  • but how about dataTEL researchers?
  • who defines the process of negotiation?
  • what are good practices?

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privacy or ethics

  • Carusi, 2008
  • anonymization is not a panacea

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information

representation

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privacy or ethics

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isomorphism

naturalism

constructionism interactionism

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privacy or ethics

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isomorphism

naturalism

constructionism interactionism

data subjects concerns researcher concerns

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privacy or politics

  • reflect on cumulative disadvantage
  • prediction becomes self-fulfilling prophecy
  • who are you observing?
  • disadvantaged groups
  • groups unlikely to articulate their rights
  • who wants your data?
  • university, law enforcement, employers

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lessons for dataTEL

  • privacy is not just confidentiality or compliance
  • compliance is “easy” and boring
  • future research
  • develop privacy practices
  • you can define the field
  • effects for used methodologies
  • the dataTEL privacy challenge may change the

way you do research

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39 Thursday, March 31, 2011