AlessandroAcq istiandRalphGrossAlessandroAcquistiandRalphGross

HeinzCollege/CyLabC i M ll U i itCarnegieMellonUniversity

ResearchsupportfromNationalScienceFoundation,U.S.ArmyR hOffi (th hC L b) C i M llResearchOffice(throughCyLab),CarnegieMellon

Berkman Fund,andPittsburghSupercomputingCenter

BlackHatUSA2009

1. Show thatSocialSecuritynumbers(SSNs)arepredictable

frompubliclyavailabledata

Knowledgeofanindividualsbirthdayandbirthplacecanbeexploitedtoinfernarrowrangesofvalueslikelytoincludethatp g y

individualsSSN

Thisisdueinparttowell meaning butcounter effective public Thisisdueinparttowellmeaning,butcountereffective,publicpolicyinitiatives

Hi hli ht i t d i k di li ti2. Highlight associatedrisksandimplications

3. Discuss possibleriskmitigatingstrategies&policies

SSNsweredesignedandissuedbytheSocialSecurityg y yAdministration(SSA)forthefirsttimein1936asidentifiersforaccountstrackingindividualearnings

Unfortunately,overtimetheystartedbeingused,andabused,asauthenticationdevices NotwithstandingwarningsbySSA,FCT,GAO,scholars,andso

forth

Naturally,thesamenumbercantbeusedsecurelybothasidentifierandforauthentication

ThewideavailabilityofSSNs andtheirdualuseas ThewideavailabilityofSSNs,andtheirdualuseasidentifiersandauthenticators,makeidentitythefteasyandwidespreadandwidespread

Knowledgeofsomebodysname,DOB,andSSNisoftensufficientconditionforaccesstofinancial medical andsufficientconditionforaccesstofinancial,medical,andotherservices Sometimes evenapplicationswithjust7outof9correctdigitsare Sometimes,evenapplicationswithjust7outof9correctdigitsare

acceptedasvalid(FTC2004)

E hSSNh di it EachSSNhas9digits: XXXYYZZZZ

andiscomposedofthreeparts andiscomposedofthreeparts: Areanumber:XXX

G b YY Groupnumber:YY Serialnumber:ZZZZ

TheSSNissuanceschemeiscomplex butnot TheSSNissuanceschemeiscomplex,butnotstochastic

Th SSAi lfh f l i bli l l di TheSSAitselfhasforalongtimepubliclyrevealeditsdetails

Thisiswellknown Thisiswellknown Infact,inferenceofthelikelytimeandlocationofSSN

applicationsbasedontheirdigitshasbeenexploitedtocatchpp g pfraudstersandimpostors

However,theSSAalsostatesthattheSSNassignmentff l dprocessis,effectively,random:

SSNsareassignedrandomly bycomputerwithintheconfinesoftheareanumbersallocatedtoaparticularstatebasedondatatheareanumbersallocatedtoaparticularstatebasedondatakeyedtotheModernizedEnumerationSystem(RM00201.060)

Alaska NewYork

First 5digitswith1guess

All9digitswith

Inthelast30years SSNissuancehasbecomemoreregular Inthelast30years,SSNissuancehasbecomemoreregular Increasingcomputerizationofthepublicadministration,including

SSAanditsvariousfieldsoffices

After1972,SSNassignmentcentralizedfromBaltimore TaxReformActof1986(P.L.99514) After1989,EnumerationatBirthProcess (EAB)

Priorto1989,onlysmallpercentageofpeoplereceivedSSNwhentheywereborntheywereborn

Currentlyatleast90percentofallnewbornsreceiveSSNviaEABtogetherwithbirthcertificate

1. WeexpectedSSNissuancepatternstohavebecomemore

regularovertheyears,i.e.increasinglycorrelatedwithan

individualsbirthdayandbirthplacey p

ThisshouldbedetectedthroughanalysisofavailableSSNdata2. Weexpectedthesepatternstohavebecomesoregularthatitp p g

ispossibletoinferunknownSSNs basedonthepatterns

detectedonavailableSSNsdetected o a a lable SS s

ThisshouldbeverifiedbycontrastingestimatedSSNsagainstknownSSNs

OutsidetheSSA,thecurrentunderstandingoftheassignmentofthefirst3digitswasincorrect,andtherelationshipbetween

demographicpatternsandthesequentiality ofthelast4digitsg p p q y g

wasunexplored

Hence,previousworkinthisareafocusedoninferringthelikelyyearor, p g y yyearsandstateofSSNissuanceofaknown SSN(e.g.,[Wessmiller,

2002],[Sweeney,2004],[EPIC,2008])

Wefocusedontheinverse,harder,andmuchmoreconsequentialinference:exploitingthepresumptivedayand

locationofSSNapplicationtopredictunknown SSNs

Alaska,1998 NewYork,1998

First 5digitswith1guess

All9digitswith

TheSocialSecurityAdministrationsDeathMasterFileisa TheSocialSecurityAdministration sDeathMasterFileisapubliclyavailabledatabaseoftheSSNsofindividualswhoaredeceased Oneofthepurposesofmakingthisdataavailablewastocombat

fraud Unfortunately,itcanalsobeanalyzedtofindpatternsintheSSN

issuancescheme WeusedDMFdatatofindpatternsintheissuanceofSSNs

bydateofbirthandStateofSSNissuancefordeceasedbydateofbirthandStateofSSNissuancefordeceasedindividuals Namely,wesortedrecordsbyreportedDOBandgroupedthemby

t dSt t fireportedStateofissuance Aniterativeprocess

Name Birth Death Last Residence SSN Issued

JOHN SMITH

21 Jun 1904

Oct 1979

33540 (Zephyrhills, Pasco, FL) 022-10-3459 Massachusetts

1. TEST1:WeusedmorethanhalfamillionDMFrecordstodetectpatternsinSSNissuancebasedonbirthplaceandstateofissuance andusedthosepatternstopredict(andstateofissuance,andusedthosepatternstopredict(andverify)individualSSNsintheDMF

2 TEST2:Wemineddatafromanonlinesocialnetworkto2. TEST2:Wemineddatafromanonlinesocialnetworktoretrieveindividualsselfreportedbirthdaysandbirthplaces,andestimatedtheirSSNsbyinterpolatingp y p gthatdatawithDMFpatterns.WeverifiedtheestimatesusingofficialEnrollmentdatausingaprotected(andIRBapproved)protocol

1. Whetherwecouldpredictthefirst5digitsofanindividualsSSNwithoneattempt

2. WhetherwecouldpredicttheentireSSNwithfewerthan10,100,and1,000attempts

Note:1,000attemptsisequivalentto3digitPIN Thatis,veryinsecureandvulnerabletobruteforcey

attacks

ME

EAB starts here (1989)CA

1973 2003

h l (f f d l ) Withasingleattempt(firstfivedigitsonly): 7%(1973 1988) 44%(19892003)

With10attempts(complete9digitSSNs): 0.01%of(1973 1988) 0 1%(19892003)0.1%(1989 2003)

With1,000attempts(complete9digitSSNs): 0.8%(19731988)

8 %( 8 ) 8.5%(1989 2003)

Theseareweightedaverages forsmallerstatesandrecentyears,predictionratesarehigher.E.g.,1outof20SSNsinDE,1996,areidentifiablewith10orfewerattempts

f InTest2weusedbirthdaydataof621aliveindividualstopredicttheirSSN,basedoninterpolationwithDMFdatadata Oursample:bornin19861990(i.e.,mostlybeforeEAB) Inmostpopulousstates(i e worstcasescenario)Inmostpopulousstates(i.e.,worstcasescenario)

Birthdayandbirthplacedatacanbeobtainedfromseveralsources,butmosteasily,andinmassamounts,fromonlinesocialnetworks ItistrivialforanattackertowritescriptstopenetrateOSN

d d l d f dcommunitiesanddownloadmassiveamountsofdata

T t fi d lt fT t (f i f Test2confirmedresultsofTest1(forsamemixofyears/statesofbirth)

ThisvalidatesthatinterpolationofSSNdatafordeceased ThisvalidatesthatinterpolationofSSNdatafordeceasedindividualsandbirthdaydataforaliveindividualscanleadtothepredictionofthelattersSSNs

ExtrapolatingtotheUSlivingpopulation,thiswouldimplyh id ifi i f d illi SSN fi di i dtheidentificationofaround40millionSSNsfirst5digitsandalmost8millionindividualscompleteSSNs

Caveat:Assumingknowledgeofbirthdata! Caveat:Assumingknowledgeofbirthdata!

l k l d Personalknowledge Onlinesocialnetworks Voterregistrationlists Voterregistrationlists Freeonlinepeoplesearchservices Commercialdatabases

Statisticalpredictionsdonotamount,alone,doStatisticalpredictionsdonotamount,alone,doidentitytheft Howcanyoutest10,100,or1,000variationsofanSSNy , , ,withoutraisingredflags? Usingbotnets anddistributedonlineservicesforbruteforceverificationattacks TumblingattackshavebeendocumentedbyIDAnalytics

Phishing Phishing SSNVS:SSNVerificationService(SSA) eVerify (DHS) eVerify (DHS) Instantcreditapprovalservices DOB/SSNmatchoftenissufficientconditiontoget DOB/SSNmatchoftenissufficientconditiontoget

approvedforseveralservices

SSN Online SSNs as Availability of Distributed predictability verification

systems Instant credit

approvalseVerify

authenticators CRAs Financial

institutions Medical

birth data Commercial

databases Free online

people

attacks Botnets

eVerify SSNVS

services []

people searches

Voter registration lists

Online social networks

d i l ih d f l Randomizeassignmentscheme(alldigits)?

Improvepersonalcomputersecurity?

Beonthealertfordistributedattacks?Improverealtimecoordination?(ID

StopusingSSNsforauthentication,reverttosingleuseasidentifiers?

Changedefaultsettings?Changeaccess/securitypolicies? coordination?(ID

Analytics2003)Improvelaxverificationprocedures?

useasidentifiers?policies?

Shortterm Randomizescheme But,thisalonenotenough:

DoesnotprotectedissuedSSNs;doesnotresolveauthenticator/identifierissue

LongtermLongterm Reconsiderlegislativeinitiativesfocusingon

redacting/removingSSNsfromdocuments/publicexposure Phaseoutauthenticationusage

Negligenceargumentforbusinessesthatusethemassuch?

Sunsetsolution? Sunset solution? E.g.,makeallSSNspublicbyyear2014 transitiontosecure,private,

efficientauthenticationmethodsinthemeanwhile?

Research support from the National Science Foundation under ppGrant 0713361, from the U.S. Army Research

Office under Contract DAAD190210389, from Carnegie MellonBerkman Development Fund, and from the Pittsburgh Supercomputing Center is gratefully acknowledged