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------=_NextPart_01C4F197.49C61310 content-location: file:///C:/2AA2647F/3A-03-Bhargava__files/pres.xml content-transfer-encoding: quoted-printable content-type: text/xml; charset="utf-8" ------=_NextPart_01C4F197.49C61310 content-location: file:///C:/2AA2647F/3A-03-Bhargava__files/slide0029.htm content-transfer-encoding: quoted-printable content-type: text/html; charset="us-ascii" MHT (IE Only)
Using Entropy to Trade <= /span>Privacy for Trust
Yuhui Zhong <= /span>
Bharat Bhargava&#= 13;
{zhon= g, bb}@cs.purdue.edu
Department of Com= puter Sciences
Purdue University=
This work is supported by NSF grant IIS-0209059
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Prob= lem motivation
nPrivacy and trust form an adversarial relationship <= /div>
nInternet users worry about revealing personal <= span style=3D'position:absolute;top:48.0%;left:14.04%;width:92.69%;height:6.5%= '>data. This fear held back $15 billion in online revenue in 2001
nUsers have to provide digital credentials that contain private informat= ion in order to build trust in open environments like Internet. <= /span>
nResearch is needed to quantify the tradeoff between privacy and trust.<= /span>
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Sub problems
nHow much privac= y is lost by disclosing a piece of credential? nHow = much does a user benefit from having a higher level of trust? = nHow = much privacy a user is willing to sacrifice for a certain amount of t= rust gain?
<= span style=3D'visibility:hidden'>n
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nFormulate the privacy-trust tradeoff problem =
nDesign metrics and algorithms to evaluate the privacy loss. We consider:
nInformation receiver
nInformation usage
nInformation disclosed in the past
nEstimate trust gain due to disclosing a set of credentials
nDevelop mechanisms empowering users to trade trust for privacy.
nDesign prototype and conduct experimental = study
Prop= osed approach
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nPrivacy Metrics
nAnonymity set without accounting for  probability distribution = [Reiter and Rubin, ’99] <= /div>
nDifferential entropy to measure how well an attac= ker estimates an attr= ibute value [Agrawal and Aggarwal ’01]
nAutomated trust negotiation (ATN) [Yu, Winslett, = and Seamons, ’03]
nTradeoff between the length of the negotiation, t= he amount of information disc= losed, and the computation effort
nTrust-based decision making [Wegella et al. ’03]
nTrust lifecycle management, with considerations o= f both trust and risk assessments=
nTrading privacy for trust [Seigneur and Jensen, ’04]
nPrivacy as the linkability of pieces of evidence = to a pseudonym; measured by usin= g nymity [Goldberg, thesis, ’00]
Relat= ed work
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nSet of private attributes that user wants to conceal
nSet of credentials
nR(i): subset of credentials revealed to receive= r i
nU(i): credentials unrevealed to receiver i&#= 13;
nCredential set with minimal privacy loss
nA subset of credentials NC from U (i)= 3;
nNC satisfies the requirements for trust building
nPrivacyLoss(NC∪R(i)) – PrivacyLoss(R(i))) is minimized
Form= ulation of tradeoff problem
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nDecision problem:
nDecide whether trade trust for privacy or not
nDetermine minimal privacy damage
nMinimal privacy damage is a function of minimal p= rivacy loss, information usag= e and trustworthiness of information receiver.
nCompute trust gain
nTrade privacy for trust if trust gain > minimal privacy damage
nSelection problem:
nChoose credential set with minimal privacy loss
Formulation tradeoff problem (con= t. 1)
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nCollusion among information receivers
nUse a global version R<= i>g instead of <= i>R(i)
nMinimal privacy loss for multiple private attributes
nnc<= i>1<= span style=3D'mso-spacerun:yes'> better for attr<= i>1 but= worse for attr<= i>2 tha= n nc2
nWeight vector {w1, w2, …, w<= i>m} corresponds to the sensitivity of attributes
nSalary is more sensitive than favorite TV show
nPrivacy loss can be evaluated using:
nThe weighted sum of privacy loss for all attributes
nThe privacy loss for the attribute with the highest weight
Form= ulation tradeoff problem (con= t. 2)
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nQuery-independent privacy loss
nUser determines her private attributes
nQuery-independent loss characterizes how helpful provided credentials for an adversarial to determine the probab= ility density or probability mass function of a private attribute.
Two = types of privacy loss
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