LinkedOn:Concerns About Data

Linkability

Mireille Hildebrandthttp://works.bepress.com/

mireille_hildebrandt/

April 21, 2023 1

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Agenda:

1. Transnational data flows

2. The computational turn: from data to ‘knowledge’

3. Traditional approaches: control, anonymity, data minimization

4. Alternative or complementary approach: Ambient Law

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Transnational data flows

• Personal data PII, relational data, other data

• SWIFT, PNRs, SNS, search engine logs (Google), cloud computing, e-Commerce, EINs, travel/transport, ehealth, mhealth

• De- and recontextualization04/21/23 4

Computational turn: from data to knowledge

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Primacy of Connectivity

• Turning data into information, generating knowledge

• Connectivity makes devices and infrastructures smart

• Linkability is where the money is (targeted advertising and servicing, price discrimination)

• Connectivity: context-awareness and pattern-recognition

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Primacy of Data Science

• ‘combination of the software programming, statistics and storytelling/art that explains the nuggets of gold hidden under mountains of data’ (Economist 27th February 2010)

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Computational Turn:

• In the sciences, e-commerce, public security, critical infrastructure, employment, healthcare

• The computational vulcano ash-cloud: real or constructed?

• ‘Thomas-theorem’: if machines define situations as real, they are real in their consequences?

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Knowledge discovery in data bases (KDD):

• interception • storage• aggregation • analysis – data mining• interpretation • application

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Traditional approaches:control, anonymity, data minimisation

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Anonymity & KDD:

• KDD often involves the mining of anonymized aggregated data; data protection directive NOT applicable

• Anonymity offers limited protection against power of KDD

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Linkability (correlatability):

• Linking data pertaining to one individual across different contexts [eg deanonymizing data; violating contextual integrity]

• Linking personal or other data to computational knowledge [eg targeted servicing; proactive environments]

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Specific Threats of Linkability:

• Undesirable identification

• Being anticipated as a specific type of person, prediction of lifestyle, earning capacity, mobility, interests, desires, recidivism; prone to use violence or to have a depression, to engage in terrorist activity, to spend in specific ways

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‘When your carpet calls your doctor’

The Economist – April 10, 2010

Trust after the computational turn:

• No inkling of who knows what about a person: hidden complexity

• Need for a novel type of literacy in the age of computation

• ‘Reading’ computational patterns

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Specific Threats of Linkability:

• Privacy (‘freedom from unreasonable constraints on the building of one’s identity’ A&R)

• Freedom from unjustified discrimination (incorrect, unfair)

• Due Process (no way to anticipate or contest how one is anticipated)

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Alternative and complementary

approach: Ambient Law

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Ambient Law

• Articulation of legal protection into the smart infrastructure• Democratic legislator should

create the incentive structure by co-designing the architecture

• Contestability of automated decisions must be built-in

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Ambient law:

• ‘Peircing the Computational Veil’• Transparency Enhancing Tools

(TETs)• ‘Auditability’ = contestability• Feedback: intuitive interfaces• Smart data minimisation

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Data Protection DirectiveD 95/46/EC

Art. 12 [and 15]

Right of access• MS shall guarantee every data subject

the right to obtain from the controller:

• Knowledge of the logic involved in any automatic processing of data concerning him at least in the case of the automated decisions referred to in art. 15 (1)

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Data Protection DirectiveD 95/46/EC

Recital 41:(…) every data subject must also have

the right to know the logic involved in the automatic processing of data concerning him, at least in the case of the automated decisions referred to in Article 15 (1); whereas this right must not adversely affect trade secrets or intellectual property and in particular the copyright protecting the software; whereas these considerations must not, however, result in the data subject being refused all information

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Technical articulation:

• Type I = based on auditing; access to or information about system from the data controller/processor

• Type II =based on counterprofiling• Inference machines• Simulation• Privacy mirrors• Playing around to guess how one is

targeted

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Facilitate productive trust

• Create intuitive interfaces for transparency enhancing tools (TETs) that provide the substance of the transparency right of art. 12

• This will enable smart instead of blind data minimisation

• As well as smart data sharing:• With whom, when, where, in which context

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Thank you for your attention

Any questions?

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