Privacy Engineering Training
Privacy Engineering TrainingFrank Dawson(frank dot dawson at Nokia dot com)2013-06-14 Frank Dawson 2013 Privacy Engineering Training-Frank Dawson1
Consumerprivacyissuesare aareRedHerring.Consumerprivacyissuesa RedHerring.You have zeroanyway,Youprivacyhave zeroprivacy anyway,so get over it! so get over it!Scott McNealy,ScottCEO SunMicrosystemsMcNealy,CEO Sun Microsystems(Wired Magazine Jan(Wired1999) Magazine Jan 1999)2
Contents Privacy contexualityWHY – Imperative for privacyWHAT – Defining the privacy intent for a projectWHAT - Information privacyHOW - Privacy engineeringPrivacy compared to security3
Privacy Contextuality4
Privacy triangle of trust5
WHYImperative for Privacy6
2013Instagram case Facebook owned photo sharing social network site proposed changing itsterms of use so it could exploit members' photographs for profit - withoutcompensating the owners Impact: Daily active users fell from almost 16.3 to about 7.6 million Brand damage: ”To do a Zuckerberg” and ”To be Instagrammed” coined Principles violated: Fair-Value Exchange, Proportionality7
2012Delta mobile app case US: CA AG warned mobile appsdevelopers they had 30 days toprovide consumer with privacy policyprior to download Delta Airline provided frequent flyerswith mobile app missing a privacypolicy Fine: USD 2500 per mobiledownload, est. 30M SkyMilesmembers Principles violated: Transparency,Choice, Consent8
2012Google tracking case Circumvented Apple privacysafeguards on Safari browsers Stanford research discoversDoubleClick over-riding cookiecontrol Millions of consumer effected FTC imposes record fine Prompts EU investigations Principles violated: Transparency,Consent, Fair & legal, Legitimatepurposes9
Average cost of unauthorized disclosureSource: us.pdf10
Why is privacy important? Authorities are doing joint-enforcement onmajor companiesExample: Facebook Canadian, US, Nordic, Irish regulatorsinvestigated complaints and foundviolations Increasing public policy maker interest inmobile technologiesExample: Positioning technologies More and more laws globally11Enforcement Actions: Fines Penalties Cost of remediation Forced privacy program 20 year external audit Deletion of unlawfullycollected data Sales stops, recalls
WHATSetting the Privacy Context12
Compliant versus Accountable Today, it is nolonger sufficient to just be compliantbut companies are being tasked to show that they areaccountable to our privacy goals Implications to your project include: 13Identify your information privacy teamGet awareness and education training for the teamSpecialty training for added key team membersPrivacy champ is identified in the product teamProducts need a privacy assessment sign-offProper and timely handling of operational issues
Elements of an Accountable privacy program1. Executive Accountability and oversight Internal senior executive oversight and responsibility for data privacy and data protection2. Policies and processes to implement them Binding and enforceable written policies and procedures that reflect applicable laws, regulations and industrystandards, including procedures to put those policies into effect3. Staffing and delegation Allocation of resources to ensure that the organization's privacy program is appropriately staffed by adequatelytrained personnel4. Education and awareness Existence of up-to-date education and awareness programs to keep employees and on-site contractors aware of dataprotection obligations5. Risk assessment and mitigation Ongoing risk assessment and mitigation planning for new products, services, technologies and business models. Periodic Program risk assessment to review the totality of the accountability program6. Event management and complaint handling Procedures for responding to inquiries, complaints and data protection breaches7. Internal enforcement Internal enforcement of the organization's policies and discipline for non-compliance8. Redress Provision of remedies for those whose privacy has been put riskNot just compliant but accountable14
Role of the information privacy team Creating lead technical roles within an organization with responsibility forimplementation and assurance that privacy is accounted for during theproduct development lifecycle is critical element of Accountability Forms basis for the operational component of an organization’s privacyprogram These roles include one or more Unit Privacy Officers and Privacy Champsacross the organization’s various business units General responsibilities focus on goal of advocating Privacy by Designprinciples in product teams through implementation and assurance activitiesrelated to privacy safeguards in an organization’s products and service Also shapes future choices in information privacy technologies byparticipating in industry collaboration and in-house technology roadmapping projects15
Unit privacy officer Responsibility and oversight for timely and proactive support to ensure Privacyby Design followed within the development lifecycle of products and services Monitors and reports on privacy compliance status to accountable executive forprivacy wihtin the organization Creates and supports a network of Privacy Champs within product teams Acts as a subject matter expert for specific information privacy competenceareas and resources Organizes and conducts privacy training & awareness within the organization Participates in identifying privacy risks and technical supportt of issue responsemanagement Contributes to the overall organizational privacy program (E.G., Privacy vision,Privacy policy, Privacy principles, Privacy safeguarding requirements, Privacyengineering processes)16
Privacy champ Role description is adjusted to product resourcing levels Typically staffed from within product team In-depth understanding of privacy requirements and ability toapply understanding to one’s own responsibility area Understand privacy requirements and represent privacy viewsinside own team/organization/responsibility area and interpretwhat the requirements mean in the context of ownresponsibility area Contribute to Privacy Impact Assessments, threat analysis Collaborates with other privacy champs17
Define the privacy intent for the product Vision: Articulates the high level aspirations to protecting thepersonal data of individuals using the product E.G., ”Consumers trust us to meet their privacy expectations” Principles: Identify which privacy principles apply to the product E.G., select from those codified in OECD, FIPP, EU frameworks Objectives and activities: Define concrete objectives andrelated activities to achieve the objectives E.G., Industry leading privacy controls built into our software byadopting Privacy by Design, E.G., Mature privacy aware culture through training and effectivegovernance and processes18
Privacy related processesIssue Response Management (IRM) Ensures that alleged and reported issues or incidents treatedproperlyPrivacy Breach Deals with alleged unauthorized access to, or collection, use ordisclosure of personal data and describes the actions that needto be taken in the case of a privacy breachAuthority Request Deals with requests by authorities for personal dataConsumer Request Ensures timely response to consumer requests to exercise theirrights E.G. access to their personal data or to delete or modifyunnecessary, incorrect or outdated personal data19
WHATInformation Privacy20
Privacy theorem Privacy impact equation PD Fn (Pi, In)Privacy impact is a function of the personal information and information nymity associated thesystem or specification under reviewed (SUR) Identifiability equation IN Fn (Id, Lk, Ob)Identifiable is a function of the identifiability, linkability and observability character of the datawithin the SUR, establishes the ”nymity” component of PII Threat equation TH pd 1.n (Fn (Pli, Ppi, Thi, Psi))Threat is a function of sumation for each personal data, of the tuples of the privacy datalifecycle context, associated privacy principle, identified threat and specific privacy safeguardapplied in the SUR Risk equation RK th 1.n (Fn (Tti, Hmi, Hpi, Rmi))Risk is a function of the sumation of the tuples of the threat type, harm magnitude, harmprobability and risk mitigation applied in the deployment of the SURREALITY CHECK: Privacy cannot yet be simplified into a few equations but this couldbecome the future, if Privacy Engineering matures into a technolical discipline21 Frank Dawson 2013 Privacy Engineering Training-Frank Dawson
Roles within the privacy framework DPA, Data Privacy Authority,Information PrivacyCommissioner, etc. is theindependent legal authority foradministering privacy rules withina country The consumer is the Data Subject The Data Controller is entity thatdetermines purposes and meansof processing consumer’spersonal data The Data Processor performsinformation processing on behalfof the Data Controller22Data ProtectionAuthority es a reference is also madeto a Third Party, which can beviewed as outside this privacyframework, but the responsibility ofthe Data Controller.
Personal data/information Personal information relates toinformation about a natural personWhen the data can be associated withan individual, it is referred to asPersonally Identifiable Information (PII)Criteria for linkability of data to anindividual is a hot-topic within theprivacy communitySensitive PII must be treated speciallyGenerally, if PII is of a racial, religious,political, sexual orientation, medicalnature, it is characterized as Sensitive;but other categories should also beconsistedAlso commonly referred to as PersonalData23These are some of thecategories of personal data toconsider when identifying thePII in your particular projectBasic data (E.G. first name,last name, mobile number)Address data (E.G. postalcode, email address)Restricted categories of data(E.G. racial or ethnic origin,religion, trade unionmembership – if allowed byapplicable law)Social networking relateddata (E.G. metadata ofpictures uploaded, siteactivity information)Location data (E.G. GPScoordinates or mobilenetwork base station ID)Identifiers (E.G. IMEI, deviceidentifiers, IP-address)Information on howindividual users are using thesystem (E.G. log files)Monetary transactions (E.G.credit card number, accountinformation)
Nymity The Theory of Nymity applies to the degree of identification; varyingalong a spectrum from full identity of the consumer to other extremeof no linkability to the consumer, at all Combinations of few characteristics often combine in populations touniquely or nearly uniquely identify some individuals; leading someprivacy advocates to doubt universality of anonymity k-anonymous coefficient is often referred to as a quantative measureof the linkability of data to an individual and a measure of the level ofanonymity Best to treat all PII with appropriate privacy controls, because overtime, addition of context can compromise current level of anonymity24
Measuring nymity – An analysis tool Identifiability A measure of degree which information is personally identifiable. Theidentity measurement takes place on a continuum, from full anonymity(the state of being without name) to full verinymity (being truly named) Linkability A measure of degree to which data elements are linkable to true nameof the data subject, where unlinkability meant different records cannotbe linked together and related to a specific personal identity. In thisregard, complex interrelations have been taken into account, as it maybe organized and/or made possible in different ways Observability A measure of the degree to which identity or linkability are affected bythe use of a system. It considers, in fact, any other factor relative todata processing (time, location, data contents) that can potentiallyaffect the degree of identity and/or linkability25
EU guidance on personal dataPersonal Data as defined by the Directive 95/46/EC (Article 2) 'shall mean anyinformation relating to an identified or identifiable natural person ('data subject');an identifiable person is one who can be identified, directly or indirectly, inparticular by reference to an identification number or to one or more factorsspecific to his physical, physiological, mental, economic, cultural or social identity’.Additionally, WP 136 and WP 175 (section 2.2) of Art. 29 Data Protection WorkingParty should be considered, which detail the concept of personal data and qualify aunique number as personal data if it is carried by a person.Sensitive Personal Data is defined by the Directive 95/46/EC (Article 8) as anypersonal data that relates to (a) the racial or ethnic origin, the political opinions orthe religious or philosophical beliefs of the data subject, (b) whether the datasubject is a member of a trade union, (c) the physical or mental health or conditionor sexual life of the data subject, (d) the commission or alleged commission of anyoffence by the data subject, or (e) any proceedings for an offence committed oralleged to have been committed by the data subject, the disposal of suchproceedings or the sentence of any court in such proceedings. Additionally, it isrecommended to consider the context, too, when determining the sensitivity ofpersonal data. Data that is not sensitive in itself may become sensitive in a specificcontext.26
Commonly referenced privacy principlesUS FIPPNotice/Awareness, Choice/Consent, Access/Participation, Integrity/Security,Enforcement/Redress (Self-regulation, Private remedies, Government enforcement)OECDCollection limitation, Data quality, Purpose specification, Use limitation, Securitysafeguards, Openness, Individual participation, AccountabilityEU Directive 95/46/ECTransparency, Legitimate purpose, Proportionality, Personal data, Processing, Dataquality (Fair & legal, Purpose-limited, Relevant, Accurate, Time-limited), Legitimate dataprocessing (Consent, Contract, Legal obligations, Vital interests, Public interest,Legitimate interests), Processing senstive informationEU-US SafeharbourNotice, Choice, Onward transfer, Security, Data integrity, Access, EnforcementISO 29100/Privacy FrameworkConsent & choice, Purpose legitimacy & specification, Collection limitation, Dataminimization, Use & retention & disclosure limitation, Accuracy & quality, Openness &Transparency & Notice, Individual participation & access, Accountability, Informationsecurity, Privacy complianceGSMA High Level Privacy PrinciplesOpenness Transparencey and Notice, Purpose and Use, User Choice and Control, DataMinimization and Retention, Respect User Rights, Security, Education, Children andAdolescents, Accountability and Enforcement27
Privacy data lifecycle Also called the Consumer DataLifecycle , it is a fundamentalcomponent of the privacyknowledge base Define the actions related topersonal data within the privacyframework When analyzing the data flow inyour specifications, you shouldalso consider the completelifecycle for the associated PII Within the EU, collection, itself isconsidered to be an act ofprocessing !28CollectionxMaintenanceTransferProcessingStorage
Privacy by Design, Accountability PbD Bake-in privacy into specifications from the beginning, rather than retrofit to existing specifications Privacy by Re-Design (PbRD) is inevitable for legacy specifications 7-Foundation Principles1. Proactive not Reactive; Preventative not Remedial2. Privacy as the Default Setting3. Privacy Embedded into Design4. Full Functionality — Positive-Sum, not Zero-Sum5. End-to-End Security — Full Lifecycle Protection6. Visibility and Transparency — Keep it Open7. Respect for User Privacy — Keep it User-Centric Is now globally included into regulations Accountability Do What You Say and Demonstrate It! Aim to achieve more than just compliance Is now globally included into regulations29
HOWPrivacy Engineering30
Privacy safeguards/controls Privacy Engineering is emergingas a discipline based on acceptedinformation privacy concepts,processes and tools similar tothose found in informationsecurity practices Based on a cycle formed byprinciples (and safeguardingrequirements), supported bytechnology safeguards or controlsand dependent on iterativevigilance to mitigate inevitableunderlying threats to inherentvulnerabilities with ons Control types include Physical,Procedural, Technical, Legaland/or Regulatory31Ref: US/DoC NIST SP-800-53 Appendix JPrivacy Control Catalog
Technical topics with privacy impact Internet protocolsInternet and web formatsData schemasWeb APIsDevice APIsWeb service definitionsBrowser plug-insProximity and connectivity standards for promoting datasharing, device coupling and service invocationCollaborative applications/servicesDevice management servicesUser experience and UI controlMobile applications and services32
Design principles that favor privacy Specification Data Management plan Data minimization Data security (confidentiality, integrity, availability)for personal data Clarity of purpose for data collection, use, storage,transfer, deletion (privacy data lifecycle) Limits on data retention Reduce the linkability of data with de-identificationtechniques Emphasis on complete product lifecycle Consumer centric privacy defaults33
Finding vulnerabilities early saves costs While applicable to information security, this also applies to information privacy Identifying vulnerabilities early in the project lifecycle can prevent unnecessarycosts when fixing security issues In this illustration the impacts are based on hypothetical cost basis but relativemagnitude of cost escalation that occurs through the application lifecycle istypical of what IBM services experienced verified across many organizations34Reference: ”Five steps to achieve success in your application security program”, IBMWhitepaper, S/1361993557 566.html,2012.
Privacy Engineering Process (PEP)Privacy RiskIdentification Nominate PrivacyChamp from within theproduct team toconduct PEP Allocate a PrivacyOfficer to support theChamp and to overseethe activity as a whole Identify key threatsand opportunities withbusiness Identify training needs Align with SecurityEngineering Processand legal support Start documentingfindings to PEPtemplatesRequirements setting Train relevant productteams Identify and documentdata flows Full threat assessmentand mitigationplanning, definerequirements. Apply Privacy Patternsand Designs (e.g.notices, settings) toconcept Verify architecture andaddress datamanagement (e.g datalifecycle, access rightsmanagement)Coding, testing,integration Supportimplementation teamswith privacyrequirements Address 3rd party dataprocessing and securityissues (e.g. Audits,agreements,instructions) Assess threats anddefine mitigations foridentified new issues. Manage exceptions,deviations, escalationsVerification Privacy and securitytesting on Beta version Fix identified issues Complete Privacy andSecurity ImpactAssessment before golive (against go-livecriteria) – OK / not OK?Support andmaintenance afterrelease Address anyvulnerability,deviation or breachidentified after release Repeat PrivacyEngineering cycle formain new releases Aim for continuousimprovement releaseafter releaseActivities across the product development lifecycle35
PIA concept Privacy Impact Assessment remains the common tool forimplementing Privacy by Design in the product creation process Major component of the Privacy Engineering Process (PEP) Wraps together design analysis, threat analysis and risk analysis Should be undertaken by members of product team PIA concept consists of the following activities:1.2.3.4.5.6.7.8.9.36Describing the product under reviewCapturing data flows between interactors;Classifying the associated personal data;Understanding the associated privacy principles;Identify inherent vulnerabilities that could threaten privacy;Adding privacy safeguards to mitigate identified threats;Working with product business team to analyze and mitigate likelihood of risks;Document findings as evidence of accountability within the product ; andVerify implementation of findings (results of analysis) regarding the product.
Privacy engineering – tools of the tradePrivacy Impact Assessment(PIA) Methodology foranalyzing project againstapplicable privacyprinciples, taking intoaccount associatedprivacy safeguardingrequirements andassessing potentialthreats that requirementmitigation withintroduction of privacysafeguards/controls,based on riskassessment to harmcaused by technology toconsumer37
PIA process steps1. Outline data flow between internal interactors within the product.2. Outline data flow between the internal interactors within the product and interactionsof external interactors through associated format, interface or protocol used by theproduct.3. Does the product collect, utilize, store, transfer, manage information that couldidentify a person? Document the classification of personal data in PIA Report.4. Does the standard collect, utilize, store, transfer, manage information that couldidentify a network connected device? Document the classification of personal data inPIA Report.5. Identify privacy principles and underlying privacy safeguarding requirements applicableto the product.6. Outline the threats created by these data flows for instances where a privacy controlmechanism can be introduced to safeguard data protection. Document these in the PIAReport.7. Document in the PIA Report specific approaches, beyond the privacy controls in #6,that will enhance privacy such as limits on collection, limits for retention, rules forsecure transfer, rules for data processors or 3rd parties dealing with responsibleconsumer data, rules for limiting identification or obsfuscation.8. Identify harms that identified threats could cause, probability of occurrence, probablemonetary impact to and mitigations to asssure harm prevention.38FINDINGS are the actionable items resulting from this analysis
Data analysis and classification Goal of data flow analysis is to be able to identify personal data within the dataflows and classify them for privacy impactExample categories of personal data might include: Basic data (e.g. first name, last name, mobile number etc.)Address data (e.g. postal code, email address)Restricted categories of data (e.g. racial or ethnic origin, religion, trade unionmembership – if allowed by applicable law)Social networking related data (e.g. metadata of pictures uploaded, site activityinformation)Location data (e.g. GPS coordinates or mobile networking positioning information)Identifiers (e.g. IMEI, device identifiers, IP-address)Information on how individual users are accessing the system (log files)Monetary transactions (e.g. credit card number, account information)Other data types (whatever does not fit in any of the other categories) User generated content, Cookies and other tracking tokens, Consents/Prohibitions,Surveys/Questionaires, Confidential communications, Search data Classifications of personal data: Additional classification questions Not identifiable, Could be identifiable, Identifiable, Sensitive and Identifiable* Description, Collector, Uses, Purpose, Transfers, Disclosures, Storage, Retention,Deletion*39”PII 2.0”, P. Schwartz and D. s/BNA-PII-FINAL.pdf
Threats come with data Therefore we model the data using a data flow diagram (DFD) Scope is the processes (your code) and all neighbouring actorsProcessExternal interactorData flowExternal interactorProcessExternal interactorTrust boundaryReferences: Open Web ApplicationSecurity Project, Microsoft TMA40Data store
Example: College library websiteReference: 10-Min DFD Class41
Example: College library user loginReference: 10-Min DFD Class42
What is threat analysis Threat analysis is about understanding privacy threats to a system, determiningharm from those threats and establishing appropriate migitations (privacycontrols or safeguards) against those harms Analyzes threats to underlying Privacy Principles at each stage of the PrivacyData Lifecycle Analysis results facilitate selection of mitigation Privacy Safeguards/ControlsWhy follow this practice? A structured approach better ensures PbD than an ad hoc approach Threat analysis allows development teams to effectively find potential privacydesign issues. Mitigation of privacy issues is less expensive when performedduring design By knowing the threats, privacy testing efforts can be focused more effectively This is a prerequisite to conducting a Risk Analysis to mitigate associated harm43
Illustrative table to capture privacy llectionTransparencyNotice & ConsentUnauthorizedcollectionData analysisPurpose verificationHidden data basesCollectionCollection limitationUnlimited collectionPurpose verificationCollection methodanalysisLack ofproportionalityProcessingPurpose specificationLegitimate purposeProcessing unrelatedto purposeFunction limitsUser participationProcessing withllegitimate purposeProcessingProcessingLack of consumercontrolOpt-out, Platformprivacy controlAutomaticprocessingProcessingSecurityData integrity faultor datamisrepresentationData integrity checkon read, writeMisrepresentationTransferLegal obligationsTransfer PII outsideEU without consentNotice & ConsentViolation of EUcitizens’ basic rightsMaintenanceAccess &participation,Individualparticipation,RedressLack of consumerredressPrivacy policyincludes process foruser redressInability to rectifyerrors44
Threat analysis in PEPNoPrivacy Engineering ProcessThreat analysis is about recognizing associated privacyprinciples understanding privacy threats to asystem, applying safeguarding controls determining risks from thosethreats andIs threatanalysisneeded?YesDefine scope ofanalysisOutputs:Model data flowsIdentify principlesIdentify threatsApply safeguardsThreatanalysisreportAssess risksDecide mitigations establishing appropriatemigitations against those risks.Done45Data flowdiagramWork itemsin team’sbacklog
Privacy review & sign offNo Decisions made in threatanalysis are reviewed If threat analysis wasskipped, was it justified? Completion of plannedmitigation actions is verifiedIs threatanalysisneeded?YesDefine scope ofanalysisOutputs:Model data flowsIdentify principlesThreatanalysisreportAre we ok withdecisionsand possiblyremaining risks?Work itemsin team’sbacklogAre they doneand verified?Assess risksDecide mitigationsDone46Data flowdiagramIdentify threatsApply safeguards Any remaining risks aresigned off by business ownerWas thisjustified?
Risk analysis Objective is to reduce the impact to the business from the exploitation of a set of threatsRisk analysis methodologies can be found that are based on business process,information security, project management, etc. ISO 31000, ”Risk Management” standardInformation Security Forum (ISF) ”Information Risk Analysis Methodology” (IRAM)Project Management Institute (PMI), Practice Standard for Project Risk Management Process utilizes the results of the threat analysis and mitigation activityProject business team responsible for completion of risk analysis and mitigation, asknowledge of the business impact is a prerequisite; but technical team provides support Risk Harm * Monetary Value * Probability of OccurrenceRisk migitation actionable steps to avoid identified harm Migitation approaches include: Do nothing, hope for the bestInform about the risk, with for example a user warning to the riskMitigate the risk by putting countermeasures in placeAccept the risk after evaluating the business impactTransfer the risk with contractual agreements or insuranceTerminate the risk, with for example shutdown the data assetSecurity risk is about harm to the company,but privacy risk is about harm to the consumer47
Privacy design patterns Describes a generic solution to a repeating problem Format for capturing and sharing design knowledge Origins in architecture, O-O Design of software in 90s, toInfoSec in 2000s and more recently to InfoPriv Essential elements (POSA format) include: Pattern name, Context, Problem, Solution, Consequences, Known Uses,Related Patterns Examples: Informed notice, Explicit consent, Policy update, Visualizing interactionfeedback & warnings There is pattern catalog work undeway at http://www.privacypatterns.org48
Potential evidence of accountability Product team management must decide which documents willwill form evidence of their accountability, such as: 49Project description,Data flow diagrams,Data classification (E.G., a Personal Information Inventory),Data management plan,Project specific supplemental privacy policies,Threat analysis and mitigation findings/report,Risk analysis and mitigation findings/report,Action item tracking tickets,Project feature backlogs,Privacy impact assessment report, andSecurity assessment report.
Privacy Comparedto Security50
Relationship of privacy to security Information Security (INFOSEC) can be viewed as control overwho may use a computer and information stored in it Information Privacy (INFOPRIV) can be viewed as control overdisclosure of computer based information and who gets accessto it Therefore, there is a very dependent relationship”You can have security without privacybut not privacy without security”51
Influence of InfoSec on InfoPriv Information privacy borrows heavily from InfoSec fororg
Jun 14, 2013 · Consumer privacy issues are a Red Herring. You have zero privacy anyway, so get over it! Scott McNealy, CEO Sun Microsystems (Wired Magazine Jan 1999) 2 Consumer privacy issues are a Red Herring. You have zero privacy anyway, so get over it! Scot
The DHS Privacy Office Guide to Implementing Privacy 4 The mission of the DHS Privacy Office is to preserve and enhance privacy protections for
U.S. Department of the Interior PRIVACY IMPACT ASSESSMENT Introduction The Department of the Interior requires PIAs to be conducted and maintained on all IT systems whether already in existence, in development or undergoing modification in order to adequately evaluate privacy risks, ensure the protection of privacy information, and consider privacy
marketplace activities and some prominent examples of consumer backlash. Based on knowledge-testing and attitudinal survey work, we suggest that Westin’s approach actually segments two recognizable privacy groups: the “privacy resilient” and the “privacy vulnerable.” We then trace the contours of a more usable
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19 b. appropriately integrate privacy risk into organizational risk; 20 c. provide guidance about privacy risk management practices at the right level of specificity; 21 d. adequately define the relationship between privacy and cybersecurity risk; 22 e. provide the capability for those in different organizational roles such as senior executives
per, we propose the first privacy wizard for social networking sites. The goal of the wizard is to automatically configure a user's privacy settings with minimal effort from the user. 1.1 Challenges The goal of a privacy wizard is to automatically configure a user's privacy settings using only a small amount of effort from the user.
The concept of \privacy by design" has gained traction in policy circles in the last decade. However, The actual design, implementation and integration of privacy protection in the engineering of products and services remains however an open question. Di erent parties have proposed privacy-by-design methodolo-
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