The document discusses how markets can both protect and erode privacy. It notes that private data is increasingly valuable and can be traded on markets for personal data and information. However, not all valuable data needs property rights protection. The document also examines how privacy is a recent invention and new technologies challenge underlying privacy assumptions. It argues for avoiding easy answers and discusses how privacy, trust, security, and identity are interrelated concepts with no simple definitions.

1. Agreeing to Disagree About Privacy:
Markets as Privacy, Identity and Trust
Mechanisms
Jonathan Cave
13 August 2012
EINS – Internet Science Network of
Excellence Summer School

2. Short version
• Privacy is a relatively recent invention
• New technologies challenge the underlying
assumptions
– What is private?
– Are data and identities the same thing?
– How does privacy relate to risk and (a sense of) security?
• Easy answers are best avoided:
– More privacy, trust, security are better
– Privacy is dead
– Identities should be strong and unique
– Collect all the data you can, then work out how to protect
or re-use them
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and Reputation Mechanisms

3. Markets play a crucial role
• Markets can protect or erode privacy – or change
the way it operates. This can lead to markets in
privacy (protection or invasion) itself.
• Private data are increasingly valuable – this can lead
to markets in personal data and information
– Some of this value is created by use of PII, and should be
shared
– Some is merely captured by technology or given away by
inattention
– My data may say something about me, people like me or
you
– Not everything of value needs to be protected by property
rights
• Privacy of action is also important and may need
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and Reputation Mechanisms

4. Outline
• Working definitions
• A networked (abstract) view of rights regimes
• Privacy as a human and/or economic right
• Social mechanisms - rights in market settings
• Privacy and markets
• Topics for discussion
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5. WORKING DEFINITIONS OF
PRIVACY
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6. Some essential building blocks
• Privacy
• Security
• Identity
• Trust
• Technical tools: games, mechanism design,
networks, lattices, partitions
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7. Privacy of what?
Privacy of action
the right to be let alone
relational privacy
informational privacy
spatial privacy corporeal privacy
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8. Privacy…
• (inter) subjectivity –
– my view, your view, others’
views
– A regress: I think that you
think that… this is private
• Hidden in plain sight –
private, invisible or beneath
notice?
• Functions of privacy
16/08/20 Internet Privacy and Identity, Trust 8
and Reputation Mechanisms
12

9. Privacy in the Information* Society
* or “Knowledge” or “Belief”
• ‘Protected space’ has evolved to include bodies, actions, history and
judgements
• Privacy as a right or an interest
– Privacy interests can be traded-off, sold or given away
– Privacy rights are
• deeper; linked to self-control, -respect and –responsibility
• limited for children, criminals, public figures
• economic (FIPP) or ‘human’ (OECD)
• Privacy is also subjective
– What infringes my privacy may be of no consequence to you
– Actions relating to privacy may trigger conflicts or open dialogue
• Either view is contingent or uncertain. Things change, but
– It is hard to claw back information
– It may be equally hard to reveal it at a later date
– Private information may involve opinion as well as fact
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and Reputation Mechanisms

10. Privacy and publicity
• We are all more or less public figures
– We cannot control what is known about ourselves
– We do not carefully choose what to reveal
– The collective judgement may be a stampede
• This may be self-fulfilling
– ‘Give a dog a bad name…’
– Particularly true where collective judgement brings us into or out of
the public eye
• Privacy may be protected by limiting access or flooding
observers
• Privacy is perhaps most important as a societal mechanism
to
– Let us act for ourselves
– Provide respite and recovery
– Provide a currency goodwill or trust
– Give us a reason to be trustworthy
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11. Identity
• Used and abused in ever more profound and ever more trivial
ways
• Multiplies
– by design or otherwise
– for good (compartmentalisation) or ill (accountability)
• Converges and coalesces through data-mining, persistence,
sharing
• How many should we have; what pulls them together or
apart?
• More identity is not always better:
– Anonymous (cash) transactions are cheap – ID costs may deter good trades
– Privacy and anonymity interests may limit ID
– Reliance on (technical) ID may crowd out finer (character) judgement
– Powerful ID is attractive and potentially corrupting
– Opting out may become widespread – or impossible
• growing tensions between (relatively) Identity, Trustphysical identity
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and increasingly fragmented useful or effective legal and
and Reputation Mechanisms

12. Trust
• If technologies and ‘new market’ institutions provide the warp of
the social fabric, trust provides the weft
• Trust means different things to people, systems and
organisations
• Trust is central to the relation of privacy and security:
– Customers must trust business security arrangements to
safeguard their privacy
– Personal privacy and system security form coffer dams
against attack
• Trust always involves an ‘incomplete contract’ –
– Monitoring dissipates the savings of trust
– Assurance (penalties) ≠ insurance (indemnities)
– Reputation and identity are informal versions
Trustworthy Untrustworthy
• Trust and trustworthiness need to be appropriately of
Trusting
Appropriate delegation,
specialisation
Enforcement costs, costs matched
adverse incidents
Excess contracting, monitoring Lost gains from trade,
Untrusting
costs; race-to-the-bottom. inappropriate risk allocation
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and Reputation Mechanisms

13. Technical elements I
• Games:
– Players, strategies, information, preferences, solution concepts
– Non-cooperative, bargaining, cooperative
• Mechanism design:
– solution concepts help us characterise outcomes of strategic
situations
– mechanism design lets us design rules to favour desirable outcomes
• Networks
– Often binary graphs (nodes connected by links, subsets of N2) – may
be necessary to consider n-ary networks (subsets of 2N);
– Links have strength, direction, duration, state dependence, salience,
subjectivity
– Links and nodes are dual
– A topology (notion of closeness) with parameters (path length,
clustering, etc.)
– Much network theory comes from electronics –emphasis on ‘shortest
Internet Privacy and Identity, Trust
needs relaxation for privacy13
paths’ and ‘nearest neighbours’ – clearlyMechanisms
and Reputation

14. Technical elements II
• More networks
– Networks are layered (people, data, ideas, things,…)
– Self-organised networks
– Epistemic networks: ‘knows’ as links
• Lattices:
– Partially-ordered sets – complete if GLB and LUB of any two elements
are in set
– Tarski theorem: isotone functions on complete lattices have fixed
points
• Partitions:
– Dividing a set into an exhaustive collection of disjoint subsets
– Used to describe information (subsets are ‘events’), rights (below)
– Partitions make a lattice – agreeing to disagree as an example of
Tarski
• Models of communication, association, behaviour
and the propagation of risk and Identity, Trust
Internet Privacy
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14

15. PRIVACY AS A HUMAN AND/
OR ECONOMIC RIGHT
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16. Transatlantic and intergenerational tussle
• EU version – privacy as fundamental human right
– Primarily data protection
– Inalienable, with emphasis on consent (‘cookie law’)
– Right to be forgotten
• US version – privacy as economic right
– Opting in/out
– Personalised or class profiling
– Three-party involvement
• Tussle – mines in the “Safe Harbo(u)r”
• Consequence – neither human right nor economic value are
protected
• Other issues
– Government involvement
– Impact of national security, crime prevention, anti-terrorism
• ACTA and DPI as a special case
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17. A NETWORKED (ABSTRACT)
VIEW OF RIGHTS REGIMES
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18. A suggested framework for rights regimes
• Rights may be seen as a lattice
– Based on a partition into ‘equivalent’ situations or outcomes
– Partially ordered by inclusion (finer distinctions)
• This creates a mechanism for communication and negotiation
– A language L to map a (set of) situations E into public action or
utterance L(E)
– First round – all parties form their judgement and ‘do their thing’
– Second round – each party refines his judgement based on what
others have done, leading to a (finer) posterior
– Process converges by Tarski to a common knowledge consensus
– Union-consistency: !∩​!↑′ =∅ %&' ((!)=((​!↑′ ) +ℎ-& ((!)=(
(!∪​!↑′ )
– If the language is union consistent, agreeing to disagree is impossible
• Can be applied to options and outcomes
• Public language – right to act
• Further partial order: preference over Identity, Trust
Internet Privacy and
actions.
and Reputation Mechanisms
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19. SOCIAL MECHANISMS -
RIGHTS IN MARKET SETTINGS
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20. Rights as property rights
• Personal data and actions have externalities – this
can lead to market failures
• Individual property rights can
– Prevent encroachment (even if non-transferable)
– Facilitate trades and bargains
– Encourage optimal use of information
– Produce strategic manipulation and distortion (‘acting out’)
• Collective property rights may be needed
– Informational commons
– Jointly private data (esp. of transactions)
– Conventions and rules
• Bundling and unbundling are vital
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and Reputation Mechanisms

21. Privacy preferences and markets
• Use of personal profiling for targeted third-party
monetisation
• What information is ‘sensitive’?
• Is data privacy linked to PII?
• Business models
– Harvest and resell behavioural data that reveals preferences
– Mine and recombine stored profile information
– ‘Nudge’ users into preferred actions
– Sell ID theft and other forms of privacy protection
– Privacy intermediaries
– Privacy as a ‘local’ public good or a social construct
– Privacy as an asset (with derivatives)
– PITs and PETs Internet Privacy and Identity, Trust 21
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22. Efficiency
• Selection vs. incentives – who should bear the risks
and costs of privacy protection?
– Balance, power to act, preferences, risk aversion,
resilience
– Repudiation and re-issue
– Protecting people from themselves
• Unintended (behavioural) consequences
– Cynicism, paranoia, opportunism leading to poor data,
absent data, crime
– Privacy as a social construct
– Crowding in and crowding out
– Changes to accountability, responsibility and transparency
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and Reputation Mechanisms

23. PRIVACY AND MARKETS
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24. Privacy affects the functioning of many
markets
• Example – open, closed and discretionary order
books in financial markets (how to interpret trade
data)
• Trust in automated transactions systems
• Exploiting asymmetries of trust – revelation of
private data as a trust-enhancing mechanism
• Strong differences in national and cultural attitudes
• Mutually assured identity
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and Reputation Mechanisms

25. Some peculiarities of the market environment
• Network effects and interoperability:
– tipping equilibrium (“Winner takes all”)
– Excess volatility or excess inertia
– Norms and conventions (cohesion vs. contagion)
• Security economics (hard shells and soft centres)
• Reluctance to exchange good and bad information
• IPR and standards
• Legal liability and public confidence
• The importance of the public sector
– Large-scale public procurement and launching customers
– Support for innovation and standardisation
– Direct, self- and co-regulation
• ‘Splitting’ between the Wild West and the Walled Garden
• Two bad puns:
– Trust and anti-trust
– Security and securities
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26. SOME EXAMPLES
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27. An example: biometrics and privacy
• Strength of biometrics can threaten privacy
– Unauthorised or invasive use of data, indirect abuse of personal data
– Inappropriate or non-consensual identification, denial of identity services
– Even if all the data are accurate, portions may give misleading or invasive
impressions
– May give away too much
– People may not be careful enough – they certainly don’t seem to be.
• Biometrics can also enhance privacy
– Mutual identification to limit inappropriate access
– May remove need for more invasive data gathering
– Protection through weakness
• Limited scalability
• Lack of interoperability standards
• Proprietary interest in collected data
• Need for cooperation and consent
– Commercial interest in offering security of data and identity-based decisions
– Technical tricks: cancellation, liveness tests, degradation
– Strongly anonymised records
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and Reputation Mechanisms

28. Some examples
• CCTV cameras in public and shared private spaces
– London has more than the whole of the US
– Every aspect of life is watched by someone
– Direct and measurable impacts – some perverse
– Backed by technology (ANPRS, face recognition, voice analysis)
– Linked to direct intervention
– Blurred public-private boundaries
• Hoodies and Niqābs
• Biometrics
• DNA
• Data mashing and other ‘recombinant’ data uses
• Loyalty cards and commercial profiling
• Virtual worlds
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and Reputation Mechanisms

29. Some examples
• CCTV cameras in public and shared private spaces
• Hoodies and Niqābs
– Religious and ‘tribal’ group identities, or personal freedom?
– Do we trust those who withhold their identities?
• In commercial spaces
• In employment
– To what extent are they chosen?
– To what extent does our reaction force their choice?
• Biometrics
• DNA
• Data mashing and other ‘recombinant’ data uses
• Loyalty cards and commercial profiling
• Virtual worlds
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and Reputation Mechanisms

30. Some examples
• CCTV cameras in public and shared private spaces
• Hoodies and Niqābs
• Biometrics
– A pervasive ‘strong’ form of identity – perhaps too strong?
– Merely physical identity
– Can be used for identification, authentication and indexing
– Confusion about technology and human factors
• Real vs. behavioural impacts
• Type I, II and III errors
– Where is the private sector?
– Privacy and utility currently protected by weaknesses in technology
• DNA
• Data mashing and other ‘recombinant’ data uses
• Loyalty cards and commercial profiling
• Virtual worlds
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and Reputation Mechanisms

31. Some examples
• CCTV cameras in public and shared private spaces
• Hoodies and Niqābs
• Biometrics
• DNA
– Like biometrics, a link to the physical body
– Unlike biometrics; persistent traces and durable ‘template’
– “Forensic” use (for legal and commercial decisions)
– May indicate more than identity (health, capabilities, kinship)
– Silent as to time, intent
• Data mashing and other ‘recombinant’ data uses
• Loyalty cards and commercial profiling
• Virtual worlds
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and Reputation Mechanisms

32. Some examples
• CCTV cameras in public and shared private spaces
• Hoodies and Niqābs
• Biometrics
• DNA
• Data mashing and other ‘recombinant’ data uses
– Refers to combination of data from different sources
– Hard to reconcile with existing privacy protections - informed consent
in a networked world
– Identification not necessary for privacy infringement
– The liability and intellectual property issues are profound and
unsolved
– Meanwhile, commercial and civil society development is racing ahead.
• Loyalty cards and commercial profiling
• Virtual worlds
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and Reputation Mechanisms

33. Some examples
• CCTV cameras in public and shared private spaces
• Hoodies and Niqābs
• Biometrics
• DNA
• Data mashing and other ‘recombinant’ data uses
• Loyalty cards and commercial profiling
– Who owns personal data (recent security breaches)?
– The practice is old; the power and scope are new
– A change in the implied client-customer relation
– Obstacle to search and competition or gateway to mass
personalisation?
• Virtual worlds
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and Reputation Mechanisms

34. Some examples
• CCTV cameras in public and shared private spaces
• Hoodies and Niqābs
• Biometrics
• DNA
• Data mashing and other ‘recombinant’ data uses
• Loyalty cards and commercial profiling
• Virtual worlds
– From transaction spaces to social networks, Second Life and
MMORPGs
– Mutual and proportionate identification
– Who is the relevant person?
– Delegated identity for transactional avatars
– The closeness of chosen identities
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and Reputation Mechanisms

35. Governance of privacy and identity
Administrations
Technology
Citizens
(consumers,
Business
communities,
civil society)
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36. TOPICS FOR DISCUSSION
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37. A warning from history
• Business, government and civil society all have strong stakes, but start
from different places
• Isolated events exert disproportionate influence
• Different agendas involved in privacy and security discussions are not
necessarily consistent
• Challenge to business is to embrace these issues and take joint
ownership
• Various scenarios are within our reach
High security Low security
High Privacy o Virtual agora or closed community o Peer-to-peer
Low privacy o The surveillance society (in theory) o The surveillance society (in practice)
• Failure through success – big data analytics
• Success through failure – learning to be careful and policy
improvement
16/08/2012

38. ADDITIONAL EXAMPLES OF
TENSION BETWEEN
INTERNET INNOVATIONS AND
PRIVACY
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and Reputation Mechanisms

39. Privacy and innovation – ST systems model
Market failures System failures
Positive externalities (spillovers) Failures in infrastructural provision
and investment
Public goods and appropriability Lock-in / path dependency failures
Imperfect and asymmetric Institutional failures
information
Market dominance Interaction failures
Capabilities failures
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and Reputation Mechanisms
Sources: Smith (1999), Martin and Scott (2000) and EC (2006)

40. Privacy and innovation – ST systems model
Market failures System failures
Function creep Failures in infrastructural
provision and investment
‘Tragedy of the data commons’ Lock-in / path dependency
failures (‘opt-in’/’opt-out’)
Transparency of data subjects Mismatch regulatory practices
versus opacity of systems and data practices
No incentives for newcomers with Interaction failures
privacy as USP
Privacy authorities and law
enforcement practices
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and Reputation Mechanisms

41. Case-study examples – Cloud Computing
Growing EU market (68B (2011)->150B (2015))
Software as a Service
Platform as a Service
Infrastructure as a Service
A model for enabling convenient, on-demand network access
to a shared pool of configurable computing resources
US NIST
Motives for adoption:
Cost reduction; cost accounting;
Time to market; greening of ICT
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and Reputation Mechanisms

42. Cloud computing - 2
• Tension 1 " Approach/solution 1
– Data controller-data processor (Art 29 " Technology (encryption):
WP; Art 17 95/46: security measures) SIENA (from IAAS to PAAS
• Tension 2 and SAAS)
– Informed choice and consent (auditing
SOC2/SOC3 including privacy and " Approach/solution 2
security) " Security as a Service
• Tension 3 " Forrester: $1.5B market
– Ownership, confidentiality and law (2015)
enforcement " BUT
• Tension 4 " Apple’s SDK for iOS only
– Appropriate SLAs (data integrity, data moderate attention for
disclosure, data preservation, data ‘concern for user’s
location/transfer, rights over services/ data’ (only in closed Walled
content, property rights and duties Garden of the Apps Store)
(Queen Mary’s univ study)
• Tension 5 " Approach/solution 3
– User expectations vs privacy " Cloud neutral approach?
– WTP vs WTA, right of ownership (data
portability), right to access (Facebook,
GMail) Internet Privacy and Identity, Trust 42
and Reputation Mechanisms

43. Case-study examples – Behavioural
targeting
Policy pressure: implementation of
more stricter ePrivacy article related to
use of cookies, June 2011
Market forecast: $4.4 b in 2012

44. Behavioural targeting - 2
• Tension 1
– Explicit and informed prior consent; (Art 29 WP; " Approach/solution 1
ePrivacy directive art. 5(3)); leads to unwanted " Policy approach on informed
effects (pop-ups) consent (‘browser settings are
• Tension 2 sufficient’)
– New intrusive technologies and tools
• Respawning (‘evercookie’) " Approach/solution 2
• HTML5 – persistent cookies
" Control instruments to users
" ‘Track me not’ browser button
• Device fingerprinting (unique consumer
identification) " ‘Advertising Icon Option’
" Transparant privacy policies
• Tension 3
– Trust and confidence
" Approach/solution 3
• Consumers show reluctance when confronted
" Different approaches
with BT practices
" Just in Time contextual
• Cookie practices hard to understand and to act
upon (Flash cookies)
advertising
" Consumers show reluctance
• Generic privacy policies not informative and too
long when confronted with BT
• Tension 4
practices
– Regulation is perceived to distort business
practices Internet Privacy and Identity, Trust 44
and Reputation Mechanisms

45. Case-study examples – Location based services
All data in device
All data in network
Linkage between media
GPS
Mash up
Location tracking
Friend finder
Google Street View
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and Reputation Mechanisms

46. Location based services - 2
• Tension 1
" Approach/solution 1
– Regulatory practices
• 2002/58/EC Harmonisation of opt-in " Soft regulatory practices:
consent and withdrawal of consent " offering opt-out Germany:
• Data Retention Directive 2006/24/EC; 244.000 citizens
financial burden on telco’s and ISPs
• Conflicting regulatory frameworks
– Definition of personal data, traffic " Approach/solution 2
data, location data " Control instruments to users
• Tension 2
" Switching off GPS button
– Strict regulatory practices
• Switzerland: blur all faces, all number
plates, sensitive facilities, clothing " Approach/solution 3
• Art 29 WP: storage of photo’s: from 12 to 6 " New technologies
months
• Tension 3 " Automatic face blurring
– Lack of user control (location tracking) technologies
• Gathering of profiles (Malte Spitz) " Number plate blurring
• Selling of aggregate data (TomTom)
• Tension 4
– Collection of sensitive data (faces, number
plates, clothing, buildings)
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and Reputation Mechanisms

47. Case-study examples – RFID
Ø Building block for Internet of
things
Ø Growth $5.03B (2009) - $5.63B
(2010)
Ø Use in multitude of domains
ü health care; cattle; pets;
logistics; ..
Ø Unique ID
Ø Limited control and choice/
consent for user
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and Reputation Mechanisms

48. RFID - 2
• Tension 1
– Awareness raising by EDPS, Art 29 WP, consumer • Approach/solution 1
groups
• Regulatory practices
• Limited awareness by consumers
• Privacy impact assessment
• Undesired disclosure of data
RFID
• EC Recommendation 12 May 2009
• Industrial PIA RFID
• Tension 2
• Approach/solution 2
– Critical approach from industry • Control instruments to users
• RFID singled out as privacy intrusive • ‘Killer application’
technology • Deep sleep mode
• Privacy problems are in back end • Transparency tools (?)
• Data encryption in chip is costly
• Disabling of RFID means limited access to after
sales services
• Tension 3
– Convergence of technologies with privacy
implications
• Biometrics (fingerprint recognition
• Corporeal intrusion (swarm technology)
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and Reputation Mechanisms

49. Case-study Biometrics
Growing market: $4.2B (2010) -
$11.2B (2015)
Largest share: finger printing
Facial, iris, voice show higher
CAGR
Market driver: homeland security
Decentralised systems
(authentication)
Centralised systems
(fraud detection, illegal and
criminal activities)
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and Reputation Mechanisms

50. Biometrics - 2
• Tension 1 " Approach/solution 1
– Storage of sensitive data " Regulatory practices
• False positives " Globalisation of regulation (US-
• Third party use VISIT/SIS/VIS …)
• Consent and choice " ICAO standardisation
• Tension 2 " Fine-tuning EU regulations (<12
– Limited accuracy age)
• Enrolment/identification (UK, NL)
• Tension 3 " Approach/solution 2
– Back-firing of public failures on " Control instruments to users
private business " Transparency tools (?)
• Distrust of decentralised
biometric systems " Approach/solution 3
– Single sign on " Offering surplus value
– Access systems " User friendliness
• Tension 4 " Speed (Single sign on)
– Public distrust of premium services " Trust (One time passwords)
• Advantageous for specific " Added value services (premium)
groups
– Air transport " Approach 4
– Banking " Demonstrating public value
" Identity fraud (double dippers)
" Crowd control
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and Reputation Mechanisms

51. Concluding the cases
• Legal/regulatory issues abound
– Stronger enforcement; stricter adherence to privacy constraints;
homeland security
– Harmonisation fails; different approaches in different countries
– Influx of different regimens (sector specific regulations, police and
judicial coordination)
• Privacy intrusions are part of public services as well! Specific uses and
misuses are hard to differentiate for the public at large (fraud detection vs
commercial use of data)
• Privacy innovation is restricted (face blurring technology, decentralised
biometrics)
• Usually soft approach: awareness raising, opt-in/opt-out offers;
transparency measures
• Business practices mostly oriented towards data collection and use;
privacy is only secondary to business models
• Companies with inherent privacy friendly approaches have modest
market shares and are not very visible to the public at large
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and Reputation Mechanisms