Tuesday 10 May 5 - 6.30 pm Level 5 Theatrette, 121 ... Five Safes Framework Safe people Safe project...
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De-Identification Tuesday 10 May 5 - 6.30 pm Level 5 Theatrette, 121 Exhibition Street Melbourne Privacy and Data Protection Week 9-13 May 2016
Transcript of Tuesday 10 May 5 - 6.30 pm Level 5 Theatrette, 121 ... Five Safes Framework Safe people Safe project...
De-Identification Tuesday 10 May 5 - 6.30 pm Level 5 Theatrette, 121 Exhibition Street Melbourne
Privacy and Data Protection Week 9-13 May 2016
Commissioner forPrivacy and Data Protection
Commissioner forPrivacy and Data Protection
Commissioner forPrivacy and Data Protection
ORANGE – PMS 1655UPBLUE – PMS 2756UPMUSEO SLAB – 100/700
Commissionerfor Privacy and Data Protection
Commissionerfor Privacy and Data Protection
Commissionerfor Privacy and Data Protection
ORANGE – PMS 1655UPBLUE – PMS 2756UPMUSEO SLAB – 100/700
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PresentersAgency Name Role
SiuMingTan ChiefMethodologistandGeneralManageroftheMethodologyDivisionattheAustralianBureauofSta@s@cs(ABS)
Dr.StephenHardy GroupleaderforDataPlaFormEngineeringatData61inCSIRO
FionaDowsley ChiefSta@s@cianofVictorianCrimeSta@s@csAgency,andAc@ngDirectorofStrategicPlanningattheDepartmentofJus@ce&Regula@on
GregGough ManageroftheDataVicAccessPolicy,DepartmentofTreasuryandFinance
Siu Ming Tan
ManagingDataConfidenBalityandAccessattheABS
10May2016
WhatshallIcover?
SomeTerminology
Legisla@verequirementonmaintainingconfiden@ality
DataU@lity&DisclosureRisk
TheFiveSafesFramework
SomeTerminology
Privacy: Requirementtorespecttheprivateinforma@onofindividuals
ConfidenBality: Requirementthatinforma@on,whetherprivateornot,bestored,keptorreleasedinamannerthatiden@fica@onofwhotheinforma@onreferstoisnotpossible
AnonymisaBon: Processtoremovethedirectiden@fiersfrominforma@on(e.g.name,address,ABN).
Un-idenBfiableinfo:Informa@ontreatedinsuchawaythatre-iden@fica@onisnotpossible
TheCensusandStaBsBcsAct,1905
Ø EveryABSofficertosignanundertakingoffidelityandsecrecy(sec@on7),
Ø Sta@s@calinforma@onnottobedisseminatedinamannerlikelytoenable
theiden@fica@onofapar@cularpersonororganisa@on(subsec@on12(2))
Ø De-iden@fica@onisnotsufficienttomeetlegisla@verequirements
Ø Releasemustnotlikelyleadtore-iden@fica@on
DataUBlityversusDisclosureRisk(I)
DisclosureRiskDataUBlity
ProtecBons
Abilityinusingthedatatodrawvalidconclusions
Ø SpontaneousRecogni@on
Ø Matchingrisk
Ø Higherriskforunitrecord
thanaggregateddata
Ø Perturba@onØ CellSuppressionØ CollapsingofCategories
Ø Sampling
Ø Recordmasking
Ø Subs@tu@onofValues
DataUBlityversusDisclosureRisk(II)
Ø Disclosureriskreducebyapplyingmoreprotec@ons,butdatau@lityis
reduced
Ø Datau@lityismaximisedifthereisnoprotec@onapplied,butdisclourerisk
issignificantlyincreased
Ø Wheretodrawthebalance?
Ø Needtothinkbeyondjustapplyingdataprotec@ons.
TheFiveSafesFramework
Safepeople
Safeproject
Safese`ng
Safedata
Safeoutput
Canthepersonbetrustedtousethedataappropriately?
Isthespecificuseofthedataappropriate?
Howdoesthemodeofaccesslimittheriskofdisclosure?
Howmuchprotec@onsaretobeappliedtothedata?
Howmuchcontrolsareappliedtoensuretheoutputisnon-disclosive?
Amul@dimensionalapproachtodisclosureriskassessment
Dr. Stephen Hardy
www.csiro.au
UBlityvsPrivacyWhyde-idenBficaBonisdifficult
DrStephenHardyAugust2015
NeVlixre-idenBficaBon
U@lityvsPrivacy|StephenHardy13|
100,000,000moviera@ngs480,000NeFlixsubscribers
Anonymised:Id–movie–ra@ng-date
200510%sample
“RobustDe-anonymiza@onofLargeSparseDatasets”,NarayananandShma@kov(2008)
IdenBfied:Name–movie–ra@ng-date
RaBngUniqueness
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8ra@ngs(2maybewrong)andadatewithin2weeksuniquelyiden@fies99%ofthepeopleintheNeFlixdatabase
U@lityvsPrivacy|StephenHardy
Mobilitydata
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“Unique in the Crowd: The privacy bounds of human mobility”, de Montjoye, Hidalgo, Verleysen, & Blondel. (2013).
U@lityvsPrivacy|StephenHardy
Uniquenessof1.5millionusers
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4loca@ons&@mesuniquelycharacterizes95%ofthepeopleina1.5mpersonmobilitydatabase
U@lityvsPrivacy|StephenHardy
UBlityvsPrivacy
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Themoredatathatislinkedtogether, themoreuniqueitbecomes
Themoredatathatislinkedtogether, themoreusefulitbecomes
But… Because…
U@lityvsPrivacy|StephenHardy
CurrentApproachestoAnonymisaBon
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• Losesvaluableinforma@on.• Cans@llbere-iden@fiedinsomecases.
2.Generalisa@on+grouping
1.Masking
FirstName:JohnLastName:Smith
Email:[email protected]:1SmithSt
Address2:Sydney,2000LastTravelDes@na@on:Spain
TravelDate:January2015
FirstName:JohnLastName:Smith
Email:Address1:
Address2:
LastTravelDes@na@on:SpainTravelDate:January2015
U@lityvsPrivacy|StephenHardy
DifferenBalPrivacy
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TunedRandomnoise
Originaldata
Removeanyperson
Noisydata
TunedRandomnoise
Noisydata
IndisBnguishable!
U@lityvsPrivacy|StephenHardy
Anonaly@x:Privacy-SafeDataRelease|RoksanaBoreli20|
CreatesSynthe@cData–WithPrivacyLevelGuaranteesHighDataGranularity(UnitRecords)forspecificanalyses
AnonalyBxPrivacyTechnology
ConfidenBalCompuBng
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Encrypted data
Encrypted data
Encrypted Analysis
Decrypted Answers
U@lityvsPrivacy|StephenHardy
DifferenBalPrivacy
Tradeoffs
22|
UBlity
Privacy
Rawdata
Masking
k-Anonymity
EncryptedComputaBon
U@lityvsPrivacy|StephenHardy
Fiona Dowsley
Greg Gough
Data for Victorians
Greg Gough Manager, DataVic Access Policy
Benefits of open data
• Increases productivity and improves personal and business decision making.
• Improves research outcomes. • Improves the efficiency and effectiveness of
government.
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Economic Value
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The Australian economy will grow by an extra $16 billion a year if government agencies make most
of their data freely available to the public.
• Stimulates economic activity and drives innovation and new services.
Open data value
Burke Road level crossing
DataVic Access Policy
• The default obligation under the Policy is for agencies to make de‑identified datasets available.
• If a dataset contains personally identifiable information, and cannot be de‑identified, it is not suitable for release under the Policy.
Open by design
• When developing or procuring a database or dataset consideration should be given in the design phase to enabling public access to the data that is suitable for release under the Policy.
Just another way of looking at ‘Privacy by design’
Examples of de-identified data use in Australia
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DataVic example of de-identified data use
32 TripRisk by Geoplex
Queensland Police example
Queensland Police example
OS examples of (de)-identified data use
Expectations?
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US Police example
NYC Taxi data
Taxi data
Further information
• Websites: www.data.vic.gov.au www.dtf.vic.gov.au
• Email: [email protected] • Twitter @data_vic • Phone: (03) 9651 1880
© State of Victoria 2016 You are free to re-use this work under a Creative Commons Attribution 4.0 licence, provided you credit the State of Victoria (Department of Treasury and Finance) as author, indicate if changes were made and comply with the other licence terms. The licence does not apply to any branding, including Government logos. Copyright queries may be directed to [email protected]
Thank you
