Introduction to Privacy and Privacy Engineering

Introduction to Privacy and Privacy Engineering Dr. Ian Oliver EIT Summer School, August 2014, Finland

Contents

� WHY learn about privacy?

�  PHILOSOPHY of privacy

�  LEGAL aspects of privacy �  ENGINEERING aspects of privacy

�  FOUNDATIONAL aspects of privacy

�  Supporting Material

WHY learn about privacy

�  The dominating issue regarding information systems at the moment

�  Increased public awareness of surveillance

�  Business and economic reasons

�  Trust

PHILOSOPHY

PHILOSOPHY �  The Right To Be Let Alone �  “The Right to Privacy” (Warren and Brandeis, 1890)

PHILOSOPHY

�  From where comes privacy?

PHILOSOPHY

�  "a person may be identified directly by name or indirectly by a telephone number, a car registration number, a social security number, a passport number or by a combination of significant criteria which allows him to be recognized by narrowing down the group to which he belongs (age, occupation, place of residence, etc.)”

� WP29: Opinion 4/2007 on the concept of personal data

PHILOSOPHY

�  Does ”privacy” exist?

�  If so, what does it mean?

�  If not, what does that mean?

PHILOSOPHY

Wisdom

Knowledge

Information

Data

Noise

PHILOSOPHY

�  Discuss: �  Personal privacy �  Information privacy �  Expectation of privacy within technology �  Social media, sharing, surveillance �  ”Nothing to Fear, Nothing to Hide” �  Limits of privacy and the acceptable loss of privacy �  Privacy as an innovator

PHILOSOPHY

�  Privacy as:

�  A Principle �  A Legal Discipline �  An Engineering Discpline �  An Economic Aspect

PHILOSOPHY

�  Privacy by Design (PbD) Principles 1.  Proactive not Reactive; Preventative not Remedial 2.  Privacy as the Default Setting 3.  Privacy Embedded into Design 4.  Full Functionality — Positive-‐Sum, not Zero-‐Sum 5.  End-‐to-‐End Security — Full Lifecycle Protection 6.  Visibility and Transparency — Keep it Open 7.  Respect for User Privacy — Keep it User-‐Centric

�  Semantic Gap Between PbD and Engineering

We concentrate here

LEGAL

�  Terminology

�  Personal Data / Personally Identifiable Data (PII) �  Sensitive Data �  Traffic Data

LEGAL

�  Compliance and Laws

�  EU Data Protection / WP29 �  US Data Protection

�  COPPA, HIPPA, SOX, Safe Harbor

�  Usage and Purpose versus Collection

LEGAL

�  Specific Examples

�  Privacy Policies �  Secondary Data Collection

�  Opt-‐in & Opt-‐out �  Defaults �  Necessity

�  Tracking �  Browser Cookies �  Data Transfers �  Data Retention

�  Conflicts �  EU-‐US Data Transfers �  Encryption or not? �  Trade Compliance �  Business need versus Personal need �  Information Assymetry

ENGINEERING

�  Case Study �  Data Flow Modelling

� Ontologies and Defintions

�  Requirements

�  Notice and Consent

�  Risk �  PETS � Maxims

ENGINEERING case study

Motivating Example High-‐Level View Detailed View

Motivating Example High-‐Level View Detailed View

ENGINEERING case study

Information systems

…for some definition of information

ENGINEERING an analogy

Information is a material

ENGINEERING data flow

Data Flow Modelling Basic Syntax Annotations: protocols, content

ENGINEERING data flow example

ENGINEERING ontologies

Ontology and Terminology

The mechanisms by which languages are agreed upon Lawyer – Engineer communication Terminological Defintions


What do the following statements actually mean?

Personal Data Personally Identifiable Data Location Data Field Data set


Semantics

ENGINEERING ontologies -‐ modelling

ENGINEERING ontologies -‐ security

( Unclassified ) Secret Confidential Internal Public

ENGINEERING ontologies -‐ information

�  Type Theory

�  Information type vs Machine type/Programming language type

�  Structures

�  Example, is { lat:float, long:float } a �  Location �  A struct of two reals? �  Neither

�  Context

ENGINEERING ontologies -‐ identifiers

ENGINEERING ontologies -‐ further…

ENGINEERING ontologies -‐ identification

Unauthenticated

Observed

Authenticated (*)

Proven

ENGINEERING identifiability

ENGINEERING requirements

ENGINEERING notice & consent

ENGINEERING notice & consent

�  Calculation of the Agreement from the DFD

ENGINEERING -‐ risk

ENGINEERING -‐ evaluating risk

�  Failure Mode and Effect Analysis

�  Root Cause Analysis �  STRIDE: Threat Assessment

ENGINEERING -‐ PETS

�  Hashing �  Encryption �  Dataset Partitioing �  Tokenisation �  k-‐anonymity

�  l-‐diversity, t-‐closeness, differential privacy

�  BASIC GOOD OLD FASHIONED SECURITY

ENGINEERING maxims

�  Don't collect what you don't use

�  If it looks like PII, it probably is PII, so treat it as PII �  Don't shock the user �  Location data isn't just GPS co-‐ordinates �  Good security does not mean good privacy, but good privacy doesn't come without good security

�  All information can be transformed and cross-‐referenced into whatever you need

�  Security through Obscurity, Privacy through PowerPoint and Policies...

FOUNDATIONAL

�  Information Theory

�  Syntax, Semantics

�  Entopy

PROJECT EVALUATION

Demonstrate:

�  Understanding of who the data subject is �  Where the data is flowing for various use cases through data flow modelling �  What:

�  is the level of identification of the data subject �  are the usages and purposes of �  are the information types being carried �  is the logical architecture or structure of the system

�  A risk analysis based on the given taxonomy of risks

SUPPORTING MATERIAL

�  The Privacy Engineer's Manifesto, Dennedy, Fox & Finneran

�  Understanding Privacy, Solove

�  Privacy in Context, Nissenbaum

�  Applied Cryptograpy, Schneier

SUPPORTING MATERIAL

Ian Oliver (2014)

Privacy Engineering: A Dataflow and Ontological Approach

ISBN:9781497569713

Twitter: @i_j_oliver

Blog: http://ijosblog.blogspot.fi

DISCUSSION

<<crossreferencing>>Thinking

Local Knowledge

References

Lecturer<<data subject>>Audience

<<speech, email, etc>>

<<weird brain processes>>

<<reading, listening>>

<<neurons>> <<neurons>>

<<speech, email, etc>>

security class: Publicinformation type:Content, Identity, Location, TemporalIdentity: authenticated (1)Provenance: UserPurpose: PrimaryUsage: Product Improvement, Future Human Lecturer

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Introduction to Privacy and Privacy Engineering

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