This document proposes a framework for asserting fine-grained trust values between users in social networks to enable more granular privacy controls. It identifies several social factors that can be used to calculate subjective trust values, including identity, profile similarity, relationships, reputation, and interactions. An ontology and implementation are presented to aggregate these trust values and grant access to personal information based on a requester's overall trust level. Future work is outlined to improve and expand the model.

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Digital Enterprise Research Institute www.deri.ie
Enabling Networked Knowledge
Fine-Grained Trust Assertions for
Privacy Management in the
Social Semantic Web
Owen Sacco, John G. Breslin & Stefan Decker
firstname.lastname@deri.org
IEEE TrustCom 2013 – Melbourne Australia Tuesday 16th
July 2013

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Enabling Networked Knowledge
Introduction
 Current Social Networks
 provide generic privacy settings for sharing information
 do not take user’s trust into account

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Introduction
In reality, we only share parts of
our information to whom we trust

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Research Contribution
In this work we focus on:
Using various methods to
automatically assert fine-grained
subjective trust values for different
Social Factors to grant or restrict
access to personal information

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Enabling Networked Knowledge
Use Case
1. Alex sends a request to view John’s personal
information
2. Alex is granted access to the information which
his trust value (asserted by John) satisfies a trust
threshold (assigned by John)
 Example: Provide John’s phone number to Alex if
he has a trust level of 0.83
Social Web Platforms
...
1
2
Alex
John

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Defining Trust
In our work trust is defined as:
“a person’s subjective belief
that another person will act
responsibly and will not
misuse the information”

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Social Factors
 Trust judgments are influenced by Social Factors:
 Past interactions with a person
 Opinions of a person’s actions
 Other people’s opinions
 Rumours
 Psychological factors impacted over time
 Life events
 and so forth
 These can be hard to compute since the
information required is limited and unavailable in
Social Networks

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Social Factors
 However, we have identified several factors that
trust can be asserted from the information
available in Social Networks:
 Identity
 Profile Similarity
 Relationship type
 Reputation in a Web of Trust
 Interactions

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Enabling Networked Knowledge
Trust Assertion Framework
Social Web Platforms
...
Alex
JohnSocial Semantic Web Platform
Trust
Manager
1
2
1
2
3 5
4
6

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Enabling Networked Knowledge
Social Semantic Web

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Trust Model
 Trust values are represented in the range of [-1,1]
 1 represents absolute trust
 0 represents either uncertainty or unknown
 -1 represents absolute distrust
 Positive values less than 1 represents trust but with an
element of uncertainty
 This also applies to negative values

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Trust Assertions Model
 Identity-based Trust
 Relies on authentication
 The WebID protocol is used as a Single Sign-On Service for
Semantic Web applications
– WebID provides users to authenticate using FOAF and X.509
certificates over SSL
 The subjective trust value is assigned to the requester
after s/he authenticates using WebID
 1 if successful, -1 if unsuccessful and 0 if aborted
 Definition 1: Identity-based Trust
Certificate(Cert,R) Profile(RP,R) Verify(Cert,RP)∧ ∧ ∧
AssertedBy(R,U) AssignTrust(IDT,R)⇒

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Trust Assertions Model
 Profile Similarity-based Trust
 Asserting trust based on the similarities between the user
profile and the requester
 Basic information attributes are not taken into
consideration
 Attributes such as work place information, interests,
connected peers and other profile attributes are compared
 Profile similarity-based trust calculation:
– denotes profile similarity subjective trust valueτ
– m denotes the matched distinct profile attributes between
the user’s profile and the requester’s profile
– a denotes the user’s distinct profile attributes

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Trust Assertions Model
 Definition 2: Profile Similarity-based Trust
∀UA(Profile(RP,R) Profile(UP,U)∧ ∧
Contain(RA,RP) Contain(UA,UP) Match(RA,UA)∧ ∧ ∧
AssertedBy(R,U)) AssignTrust(PST,R)⇒
 where PST {-1,1}∈

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Trust Assertions Model
 Relationship-based Trust
 The Social Semantic Web provides the user to enter a value
of how much s/he trusts that particular relationship type
 Definition 3: Relationship-based Trust
∀URT(Profile(UP,U) Contain(URT,UP)∧ ∧
Relationship(R,URT) AssertedBy(R,U)) AssignTrust(RLP,R)∧ ⇒
 where RLP {-1,1}∈

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Trust Assertions Model
 Reputation-based Trust
 Consists of a trust value of a user within a Social graph
based on all trust values given by other users
 Reputation-based trust value is the weighted average value
of all trust values given to a user
– The user’s trust values assigned within a network
– The weights denotes the reputation value of the person that
assigned the trust value
 Reputation-based trust calculation:
– τ denotes reputation trust
– w denotes the reputation of the user assigning a subjective
trust value to the requester
– v denotes the requester’s subjective trust value assigned by a
user

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Trust Assertions Model
 Definition 4: Reputation-based Trust
SocialGraph(SG,U) Contain(R,SG) Measure(RV,SG)∧ ∧ ∧
Reputaion(RV,R) AssertedBy(R,U) AssignTrust(RPT,R)∧ ⇒
 where RPT {-1,1}∈

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Trust Assertions Model
 Interactions-based Trust
 Consists of users sharing microblog posts, comments,
photos, videos, links and other content with their peers
 Interactions-based trust calculation:
– denotes interactions trust valueτ
– r denotes the number of interactions between the requester
and the user
– u denotes the number of all the user’s interactions in the
Social Web platform

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Trust Assertions Model
 Definition 5: Interactions-based Trust
∀UI(Profile(UP,U) Contain(UI,UP) Interaction(R,UI)∧ ∧ ∧
AssertedBy(R,U)) AssignTrust(INTT,R)⇒
 where INTT {-1,1}∈

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Trust Assertions Model
 Aggregating Subjective Trust Values
 To assign a fine-grained user’s subjective trust value to a
requester
 An average of all the subjective trust values of a requester
from each social factor assigned by the user
 Aggregate subjective trust calculation:
– denotes the aggregated subjective trust valueτ
– s a subjective trust value asserted based on a social factor

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Trust Assertion Ontology
(TAO)
http://vocab.deri.ie/tao

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Information Confidentiality
http://vocab.deri.ie/ppo

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Implementing Trust
Assertions

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Results

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Future Work
 To show how this model is useful for other
scenarios (for example for Recommender Systems)
 To calculate automatically Relationship-based Trust
values
 To take into account the trust values of Certificate
Authorities when calculating Identity-based Trust
 To compare our model with other state-of-the-art
models

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Enabling Networked Knowledge
Thanks!
Email: owen.sacco@deri.org