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Context, Causality, and Information Flow: Implications for Privacy Engineering, Security, and Data Economics

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The creators of technical infrastructure are under social and legal pressure to comply with expectations that can be difficult to translate into computational and business logics. The dissertation presented in this talk bridges this gap through three projects that focus on privacy engineering, information security, and data economics, respectively. These projects culminate in a new formal method for evaluating the strategic and tactical value of data. This method relies on a core theoretical contribution building on the work of Shannon, Dretske, Pearl, Koller, and Nissenbaum: a definition of information flow as a channel situated in a context of causal relations.

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Context, Causality, and Information Flow: Implications for Privacy Engineering, Security, and Data Economics

  1. 1. Context, Causality, and Information Flow: Implications for Privacy Engineering, Security, and Data Economics A presentation of doctoral dissertation research by Sebastian Benthall UC Berkeley School of Information
  2. 2. Outline ● Motivation ● Overview of Projects ● Project #1: Contextual Integrity through the Lens of Computer Science ● Disciplinary Bridge ● Project #2: Origin Privacy: Causality and Data Protection ● Project #3: Data Games and the Value of Information ● Concluding remarks THIS IS A FUN INTERACTIVE TALK: An image slide means it is time to ask a question. I’ll answer one question per picture.
  3. 3. Motivation
  4. 4. Four modalities regulating cyberspace. (Lessig, 2009) Social Norms Market Law Technology
  5. 5. Social Norms Market Law Technology Tech Industry Public Regulation
  6. 6. Social Norms Market Law Technology Tech Industry Public Regulation a2 + b2 = c2 “Words, words, words…” CS, Statistics, EE Law Social Philosophy Economics
  7. 7. Social Norms Market Law Technology Tech Industry Public Regulation a2 + b2 = c2 “Words, words, words…” CS, Statistics, EE Law Social Philosophy Economics
  8. 8. Social Norms Market Law Technology Tech Industry Public Regulation a2 + b2 = c2 “Words, words, words…” CS, Statistics, EE Law Social Philosophy Economics #?!$?!@?!*?!
  9. 9. Social Norms Market Law Technology Tech Industry Public Regulation a2 + b2 = c2 “Words, words, words…” CS, Statistics, EE Law Social Philosophy Economics #?!$?!@?!*?!
  10. 10. Social Norms Market Law Technology Tech Industry Public Regulation a2 + b2 = c2 “Words, words, words…” CS, Statistics, EE Law Social Philosophy Economics #?!$?!@?!*?! We can’t ignore this problem.
  11. 11. Overview
  12. 12. Social Norms Market Law Technology CS, Statistics, EE Law Social Philosophy Economics #?!$?!@?!*?!
  13. 13. Social Norms Market Law Technology CS, Statistics, EE Law Social Philosophy Economics
  14. 14. Social Norms Market Law Technology Project #1 “Contextual Integrity through the Lens of Computer Science” CS, Statistics, EE Law Social Philosophy Contextual Integrity Economics ...a typical I School dissertation... Surveys the use of Contextual Integrity, a theory of privacy norms, in Computer Science. Identifies theoretical gaps in CI and opportunities for innovation in privacy CS.
  15. 15. Social Norms Market Law Technology Project #2 “Origin Privacy: Causality and Data Protection” CS, Statistics, EE Law Social Philosophy Economics ...a typical I School dissertation... Identifies a information flow restrictions in law based both on semantics and origin. Resolves this ambiguity through theoretical contribution: situated information flow. Shows application of this contribution to information security in embedded systems.
  16. 16. Social Norms Market Law Technology Project #3 “Data Games and the Value of Information” CS, Statistics, EE Law Social Philosophy Economics ...a typical I School dissertation... Invents data economics to fill gap in economic theory. Theoretical contribution: data games, mechanism design for information flow. Answers: what is the value of information? Demonstrates with several examples.
  17. 17. Social Norms Market Law Technology CS, Statistics, EE Law Social Philosophy Economics ...a typical I School dissertation... problem solved :-p
  18. 18. Project #1: Contextual Integrity through the Lens of Computer Science Social NormsTechnology
  19. 19. Contextual Integrity through the Lens of computer science Sebastian Benthall Seda Gürses Helen Nissenbaum A presentation of S. Benthall, S. Gürses and H. Nissenbaum. Contextual Integrity through the Lens of Computer Science. Foundations and Trends in Privacy and Security, vol. 2, no. 1, pp. 1–69, 2017 Included as Chapter 2 of Sebastian Benthall’s doctoral dissertation titled. “Context, Causality, and Information Flow: Implications for Privacy Engineering, Security, and Data Economics”
  20. 20. Project Goals ● Characterize different ways various CS efforts have interpreted and applied Contextual Integrity (CI); ● Identify gaps in both contextual integrity and its technical projection that this body of work reveals; ● Distill insights from these applications in order to facilitate future applications of contextual integrity in privacy research and design. “Making CI more actionable for computer science and computer scientists.”
  21. 21. Background: What is Contextual Integrity? A social philosophy of privacy developed by Helen Nissenbaum. ● Privacy is appropriate information flow. ● Appropriateness depends on social context; social contexts have information norms. ● Norms are adapted to societal values, contextual purposes, and individual ends. ● Norms are structured with five parameters: ○ (1) Sender, (2) Receiver, (3) Subject, (4) Attribute, (5) Transmission Principle Example: In the health care context, there is a norm that when (1) a patient gives information about (3) their (4) health to (2) a doctor, that information is treated confidentially.
  22. 22. Background: Context in computing and policy ● Contextual Integrity: ○ Privacy as appropriate information flow according to contextual norms. First paper: 2004.. ○ Uptake in computer science since 2006. ● Context in ubiquitous computing ○ An earlier computer science research tradition, pioneered by e.g. Dey in 2001 is also concerned with privacy ○ “Context” refers to a situation: facts about the user, computer, environment. Location, identity, state… ○ Dourish (2004) publishes a critique, arguing for interactional (not representational) context in UbiComp. ● Context in policy ○ Excitement about privacy (FTC, White House, WEF) as respect for context motivates computer science interest in Contextual Integrity... ○ … but within CS, multiple traditions are blended together.
  23. 23. Study: research method ● Developed analytic template based on research questions. ● Searched for CS papers that claim to be using CI. (We found 20) ● Applied analytic template systematically to each paper. ● Used results to derive answers to each research question. A systematic review of computer science literature using Contextual Integrity.
  24. 24. Study: research questions ● RQ1. For what kind of problems and solutions do computer scientists use CI? ○ Particular subfields of CS. ● RQ2. How have the authors dealt with the conceptual aspects of CI? ○ Social contexts, norms with specific parameters… ● RQ3. How have the authors dealt with the normative aspects of CI? ○ Norms are derived from social contexts, which are adaptations of a differentiated society. ● RQ4. Do the researchers expand on CI? ○ Where do CS researchers need to fill gaps or add to CI to make concrete systems work?
  25. 25. Results: RQ1 Architecture CS researchers used CI across a few classes of technical architecture. ● User interfaces and experiences. These focus on an individual user’s activity and preferences, rather than social norms. ● Infrastructure. Catering to a large set of users and diverse applications. ○ Social platforms. Technology that spans multiple social contexts. ○ Technical platforms. Technology that mediates many different other technologies. What about the operators of these platforms? ○ Formal models. Frameworks to be used in design, but without implementation details. ● Decentralization. Decentralized architectures mirror complexity of society itself. An interesting area for future research.
  26. 26. Findings: RQ1 Architecture Theoretical Gaps: - “Modular Contextual Integrity”, faceting CI and giving guidelines for design and research at specific levels of the technical stack - Specific guidance for infrastructure design Calls to Action: - Be explicit about how system is situated among other actors (operators, moderators, etc.) - Develop formal models that connect user preferences with contextual norms
  27. 27. Results: RQ2 What did they mean by context? CS researchers had varying understandings of ‘context’’; e.g. sphere vs. situation. Substantiality Abstract: Hospitals in general. Concrete: Mount Sinai Beth Israel hospital. Domain Social: A classroom with a teacher and students is a social context. Technical: A language education mobile app. Valence Normative: A conference Code of Conduct is an account of norms inherent in a context. Descriptive: A list of attendees, keynote speakers, and program committee members is a description of the context. Stability (Dourish, ‘04) Representational: The Oval Office in the White House is an easily represented context.. Interactional: A flash mob is an interactional context.
  28. 28. Findings: RQ2 Contexts Theoretical Gaps: - CI needs an account of how social spheres connect to sociotechnical situations - What about interactional contexts? Calls to Action: - Specifically address how ‘context’ is used, and when technology bridges two or more meanings of the term - Detail flows of information to third parties; what context is that?
  29. 29. Results: RQ3 Source of Normativity CI is specific about where norms come from: social adaptation to ends, purposes, and values within differentiated spheres of society. CS papers have not adopted this source of normativity entirely. Instead, they use: ● Compliance and Policy. System is designed to comply with existing laws and policies. ● Threats. System is designed with a Threat Model, typical of security research. ● User preferences and expectations. Individual user preferences and/or expectations solicited. ● Engagement. Users interact with system to determine norms dynamically.
  30. 30. Findings: RQ3 Normativity Theoretical Gaps: - Connect CI’s metaethical theory with concrete sources of normativity familiar to CS - Spheres to threats? - Spheres to user expectations? - Spheres to the law? Calls to Action: - Measuring norms, not expectations - Supporting user engagement around identifying norms - Technical solutions for handling conflicts over norms
  31. 31. Results: RQ4 Expanding CI ● Technological adaptation to changing social conditions. ● Technology operating in multiple contexts at once, or addressing context clash, where activity in different contexts interact. ● Addressing the temporality and duration of information, and its effect on privacy ● User decision making with respect to privacy and information flow controls.
  32. 32. Findings: RQ4 Expanding CI Theoretical Gaps: - Develop account of normative change and adaptation - Address the questions around multiple interacting contexts - Address time: duration of information, forgetting, etc. - What about user choice? Calls to Action: - More modeling CI from information theory, information flow security - CI and differential privacy?
  33. 33. Disciplinary Bridge
  34. 34. Bridge: Themes from Project #1 Contextual Integrity needs to be expanded... ● ...to account for social and technological platforms that span multiple social spheres, perhaps by introducing an “operator” context. ● ...to account for more of the meanings of “context” that range from abstract social spheres to concrete sociotechnical situations. ● ...for clarity on how social norms form to reflect ends, purposes, and values in society, and the relationship between these norms and the law. ● ...to address the challenging cases where multiple social contexts collide or clash.
  35. 35. What we get… … life and technology make things complicated. Bridge: Dealing with context collisions What society wants from privacy... Professional Personal Med Edu Fin Professional Personal Med Edu Fin
  36. 36. The problem with information semantics Contextual Integrity says there are five parameters of an information norm: Sender, Receiver, Subject, attribute, and Transmission Principle. [Patient, Doctor, Patient, Health, Confidentiality] But... information topics are indeterminate. E.g.:
  37. 37. What does information mean? ● In CI, information gets its meaning from its context: how actors in roles normatively communicate with each other. ○ The meaning of information and the contextual practices are mutually constitutive. ● When information flows in a new way (between situations), that information gets new meanings. ○ E.g. When your relatives see a Facebook post intended for friends, it gives them the opportunity to make judgments about you that were not the intended meaning. ● Technical context collapse is challenging not because it violates norms, but because it is beyond our social understanding but creates information flows with new social meaning that may be disruptive to social life.
  38. 38. What does “information” mean? We have gotten this far without a definition or theory. No wonder things are so #?!$?!@?!*?!.
  39. 39. What does “information” mean? According to Dretske (1981) (epistemology, philosopher of mind) building on Shannon (1948), information is a naturalistic and causal property: Information that P is the message/signal needed for a suitably equipped observer to learn P, due to the nomic associations of the signal with P. Nomic means “law-like”, as in scientific law. The red light carries the information that the train is coming because (lawfully, regularly) the red is light if and only if the train is coming.
  40. 40. In the following projects we will update Dretske’s theory. Using insights from statistics and computer science, we will arrive at a specific formal concept of situated information flow for cross-disciplinary use.
  41. 41. Bayesian Networks Bayesian Networks (BN) are a formalism for representing the relationship between random events. A BN has: ● A directed, acyclic graph of nodes, representing random variables, connected by edges ● A conditional probability distribution (CPD) for each node, which is the probability distribution of its random variables, conditional on its parent. Together. these define a joint probability distribution over all the random variables, with some important independence relations qualitatively inferable from the graph. A C D B E
  42. 42. What is information flow, really? Pearl’s (2000) system for understanding causality is widely acknowledged and applied in statistics, philosophy, machine learning, cognitive psychology, social science research methods, … Events are part of a causal structure represented as a directed acyclic graph. This structure determines the conditional dependency of events on each other. Recession Earthquake Burglary Alarm
  43. 43. What is information flow, really? The alarm carries information about earthquakes, burglaries, and recessions. (Topics are indeterminate). In this model, the recession and earthquakes are conditionally independent. I(Recession, Earthquake) = 0 (Carry no information about each other; have no mutual information.) Recession Earthquake Burglary Alarm
  44. 44. What is information flow, really? The alarm carries information about earthquakes, burglaries, and recessions. (Topics are indeterminate). In this model, the recession and earthquakes are conditionally dependent if we know the alarm has gone off. Recession Earthquake Burglary Alarm
  45. 45. Information flow: a unified model 1. Privacy is appropriate information flow. (Nissenbaum) 2. Information flow is a message or signal from which something can be learned because of nomic association. (Dretske) 3. The nomic associations are the conditional dependencies derived from causal structure. (Pearl) The meaning of data is a function of the processes that generated it, and their context.
  46. 46. Causality Recession Earthquake Burglary Alarm
  47. 47. Causality What makes these causal models is Pearl’s do-calculus: an intervention on an event severs the links from its parent nodes. An intervention can made by anything exogenous to the model. Recession Earthquake Burglary Alarm do
  48. 48. Causality “But this isn’t causality! What about Rubin, treatment effects, randomized controlled experiments, ….” - an economist in the audience Pearlian causation fits how we experience and reason about causality (e.g., Sloman). Interventionist causation has support from philosophers (e.g., Woodward). It is compatible with other methods of causal inference and model fitting (e.g., Gelman). It is used widely in social sciences like demography and sociology (e.g., Elwert). It is the consensus view. We should use it!
  49. 49. situated information flow is a causal flow between events in the context of other causal relations.
  50. 50. Project #2: Origin Privacy: Causality and Data Protection Law Technology
  51. 51. Origin Privacy: Causality and Data Protection Sebastian Benthall Anupam Datta Michael Tschantz A technical report. Included as Chapter 4 of Sebastian Benthall’s doctoral dissertation titled. “Context, Causality, and Information Flow: Implications for Privacy Engineering, Security, and Data Economics”
  52. 52. Origin Privacy: Highlights ● Policy motivations: ○ Origin and topic information flow restrictions in the law ○ Bounded information processing systems ● Use the theory of situated information flow ● Model a system embedded in its environment ● This uncovers an interesting class of security threats due to a confusion between caused and embedded inputs. (There’s a lot more in the chapter…)
  53. 53. Origin Privacy: Policy requirements: HIPAA The HIPAA Privacy Rule defines psychotherapy notes as notes recorded by a health care provider who is a mental health professional documenting or analyzing the contents of … [a] counseling session Psychotherapy notes are more protected than other protected health information, intended only for use by the therapist. These restrictions are tied to the provenance of the information: the counseling session.
  54. 54. Origin Privacy: Policy requirements: GLBA The Privacy Rule protects a consumer's "nonpublic personal information" (NPI). ● any information an individual gives you to get a financial product or service (e.g., name, address, income) ● any information you get about an individual from a transaction involving your financial product(s) or service(s) (for example, the fact that an individual is your consumer or customer), ● any information you get about an individual in connection with providing a financial product or service (for example, information from court records or from a consumer report). There are origin requirements, but also more general subject requirements. (from FTC.gov)
  55. 55. Claim: policies use origin and meaning based information flow restrictions in an ambiguous way because: (1) real information flow is situated (2) the causal context determines: - The origin - The nomic associations (meaning) Policy ambiguity problem solved! Pregnancy Purchases Advertisements
  56. 56. Policy requirements: PCI DSS “The PCI DSS security requirements apply to all system components included in or connected to the cardholder data environment. The cardholder data environment (CDE) is comprised of people, processes and technologies that store, process, or transmit cardholder data or sensitive authentication data.” PCI DSS determines the domain in which it applies in terms of the physical connections between components. Could PCI DSS be enforced or complied with if it applied to system components unconnected from the CDE?
  57. 57. Environment Embedded Causal System (ECS) model System
  58. 58. Environment Embedded Causal System (ECS) model SH SL AH AL Is this system secure? AL independent of SH ?
  59. 59. Environment Embedded Causal System (ECS) model SH SL AH AL Is this system secure? AL independent of SH ? ✔
  60. 60. Environment Embedded Causal System (ECS) model SH SL AH AL Ei EL EH Is this system secure? AL independent of SH ?
  61. 61. Environment Embedded Causal System (ECS) model SH SL AH AL Ei EL EH Is this system secure? AL independent of SH ?
  62. 62. Environment Embedded Causal System (ECS) model SH SL AH AL Ei EL EH Is this system secure? AL independent of SH ? X
  63. 63. Environment Embedded Causal System (ECS) model SH SL AH AL Ei EL EH Is this system secure? AL independent of SH ? do do
  64. 64. Environment Embedded Causal System (ECS) model SH SL AH AL Ei EL EH
  65. 65. The point ● Probabilistic modeling of situated information flows can express both an information processing system and its environment. ● Different security properties can be mapped onto this model and onto different model conditions (interventions, observations) ● This gives us a fine-grained way to do compliance engineering.
  66. 66. Project #3: Data Games and the Value of Information MarketLaw
  67. 67. The story so far... ● Social expectations of privacy may be expressed as norms of information flow indexed to social spheres (Contextual Integrity)... ● … but our situation today is messy; our contexts collide because of our technical infrastructure. ● Our complex situation means that we have lost control over what our information means. Topics (part of the structure of norms) are indeterminate. ● Moving forward, we should scaffold our theory of privacy with situated information flow, and build up to normative theory.
  68. 68. Data Games and the Value of Information Goals (narrow version): ● Address the problem raised by Chapter 1 about how to model and design for cross-context information flows in infrastructure… ● ...using the insights about situated information flow… ● ...to understand the economic impact of data protection laws.
  69. 69. Social Norms Market Law Technology Tech Industry Public Regulation a2 + b2 = c2 “Words, words, words…” CS, Statistics, EE Law Social Philosophy Economics #?!$?!@?!*?!
  70. 70. U.S. Data Protection Laws ● Intellectual property laws ○ Since Feist v. Rural (1991), data (facts) are not protected by copyright. ○ Samuelson (2000) argues intellectual property won’t work for privacy because property is alienable, but privacy rights aren’t. ● Confidentiality and sectoral privacy laws. HIPAA, GLBA, FERPA, attorney-client privilege. ○ All tied to specific sectors or spheres. ○ They do reduce information flow outside of the situations where they apply. ○ But they do not regulate “the gaps” ● FTC notice-and-consent self-regulatory standard ○ Paradox: the more technically and legally detailed the notices, the more ignorant the consent! ○ Nobody thinks this is working.
  71. 71. E.U. General Data Protection Regulation ● It’s based on privacy rights. (Compare with IP) ○ Sort of like a property right, but different. ● Omnibus. It covers all the cases. (Compare with sectoral laws) ● New obligations protect the rights: ○ Data minimization says don’t keep or process data for no agreed upon reason. ○ Also some general exceptions to data protection, which may erode the protections... ● Consent is given to particular purposes of use. ○ A purpose is less complicated than legalease or technical data flow, so better notices? Purpose-binding in the GDPR is reminiscent of Contextual Integrity, but is based on rights not norms.
  72. 72. Law and economics for data? ● There is an important legal tradition of law and economics, using economic theory to inform legal judgments. ● Do we have one for the data economy? ● To better design data protection policy (and antitrust? etc.), we need economic theory that captures the economic impact on everyone involved (data processors, data subjects, and others)...
  73. 73. Data Games and the Value of Information Goals (real agenda): ● Using the insights about situated information flow… ● … develop a new tool, data games, for understanding the value of information ... ● … to start a new field of inquiry, data economics, that can better understand the foundational principles of the information economy! “Surely, that has been done before,” you say.
  74. 74. Contextualism in privacy economics ● “The Economics of Privacy,” by Acquisti, Taylor, and Wagman (2016) surveys the existing privacy literature. ● They judge that economics can only ever deal with privacy in a contextually specific way. ● While this sounds nice, it runs into the same problem as CI! Namely, … ● We know the most important practice in the data economy is date reuse, i.e., use of data collected in one context for another!
  75. 75. Something new! ● We need a new data economics! Really! ● We need a way to model the outcomes of creating and destroying information flows between strategic actors. ● The difference in outcome for each actor is the value of information.
  76. 76. We need a new tool to measure the value of information.. We will start with situated information flow and add features for game theory and mechanism design. We can call this new tool a data game
  77. 77. Multi-Agent Influence Diagrams (MAIDs) MAIDs: Bayesian Networks + game theory. (Daphne Koller & Brian Milch, ‘03) They have a set of agents and a directed acyclic graph with three kinds of nodes: Chance variables. Random variables with a CPD conditioning on their Parents in the graph. Decision variables. The have an associated player. They do not have a CPD (this is chosen by the player later). Utility variables. These have a CPD and an associated player. They may not have any descendants. X Y Z
  78. 78. Multi-Agent Influence Diagrams (MAIDs) To “play” a MAID: 1) Each player simultaneously chooses a CPD for every decision node they control. This is their strategy profile, σ. 2) The strategies induce the MAID into a Bayesian Network, which is sampled. 3) The sum of the sampled values of each player’s utility variables are awarded as payoffs. W X1 Z1 X2 Z2 Y
  79. 79. Multi-Agent Influence Diagrams (MAIDs) W X1 Z1 X2 Z2 Y W X1 Z1 X2 Z2 Y σ σ +
  80. 80. Data Games: Optional Edge An optional edge (dotted arrow) implies the diagram represents two different games, an open and a closed case. Note that the edge is an information flow. We can now reason systematically about consequences of allowing an information flow. (This is mechanism design). W X1 Z1 X2 Z2 Y
  81. 81. Let’s look at two data games for economic contexts that are well-understood
  82. 82. Example: Principal/Agent V B Up Ua Earliest privacy economics argument (?) by Richard Posner (1981): Employers depend on information about potential hires (V) to make efficient decisions (B). More privacy means less information means less efficiency.
  83. 83. Example: Principal/Agent E(U) Open Closed Principal (E(X | X > w) - w) P[X > w] (x - w)[E(X) > w] Agent w P[X > w] w[E(X) > w] Agent, x > w w w[E(X) > w] Agent, x < w 0 w[E(X) > w] V B Up Ua
  84. 84. Example: Price discrimination V R B Uf Uc An important economic use of personal information is price differentiation (Shapiro and Varian, 1998). The firm chooses its price (R) with or without knowledge of the consumer’s demand (V). The consumer chooses whether or not to buy (B) after getting the price.
  85. 85. Example: Price discrimination E(U) Open Closed Firm x - ϵ z* P[X > z] Consumer ϵ (x - z*)P[X > z] Consumer, x > z* ϵ x - z* Consumer, x < z* ϵ 0 z* = argmaxz E[z P(z < x)] V R B Uf Uc
  86. 86. Let’s look at two data games for economic situations that have not yet been studied
  87. 87. Example: Expert Services V C R A Uf Uc W The expert knows some specialized facts about the world (W) (i.e., medicine, law, the web). The client’s personal character traits (C), determine the value of each of m actions. The expert, who may or may not know the character traits, provides a recommendation (R). The consumer takes an action (A). The incentives of the expert and the client are aligned (no conflicts of interest).
  88. 88. Example: Expert Services V C R! A Uf Uc W First observation: If the domain of R allows for enough bits (in the Shannon sense) for the expert to encode all the information in W, then personalization is irrelevant. This demonstrates a fundamental link between Shannon information theory and data economics: information bottlenecks matter.
  89. 89. Example: Expert Services V C R A Uf Uc W First observation: If the domain of R is narrow compared to the expert knowledge W, then personalization (an open edge C -> R) does improve outcomes for the expert and client. The value of a personalized service is efficient dissemination of information through a small channel.
  90. 90. Example: Context Collision V’ B’ Up Uc D R B Uf Uc W
  91. 91. Cross-context flows: the point Data is valuable not as a good, but as a strategic resource. It’s not consumed; it is part of the structure of the game of social and economic relations itself. Market externalities are the rule, not the exception. Traditional theories of market equilibrium, industrial organization, etc. are not going to cut it. We need to start the field of data economics.
  92. 92. Tactical vs. Strategic Flows ● When considering an optional information flow, we can compare the equilibrium outcomes of the open and closed cases. Call this the strategic consequences of the information flow. ● We can also consider the outcome of opening the flow, while keeping the closed equilibrium strategy for all players except the recipient of the information. Call this the tactical consequences of the flow. We can use this to make sensitive distinctions about, e.g. the effects of a data breach vs. the chilling effects of ongoing surveillance. A data economics for cybersecurity?
  93. 93. Thank you.

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