Data and Applications SecuritySecurity and Privacy in Online Social Networks Murat Kantarcioglu Bhavani Thuraisingham Thanks to Raymond Heatherly and Barbara Carminati for helping in slide preparations November 2010
Outline • Introduction to Social Networks • Properties of Social Networks • Social Network Analysis Basics • Data Privacy Basics • Privacy and Social Networks • Access control issues for Online Social Networks
Social Networks • Social networks have important implications for our daily lives. • Spread of Information • Spread of Disease • Economics • Marketing • Social network analysis could be used for many activities related to information and security informatics. • Terrorist network analysis
Enron Social Graph* * http://jheer.org/enron/
Emergence of Online Social Networks • Online Social networks become increasingly popular. • Example: Facebook* • Facebook has more than 200 million active users. • More than 100 million users log on to Facebook at least once each day • More than two-thirds of Facebook users are outside of college • The fastest growing demographic is those 35 years old and older *http://www.facebook.com/press/info.php?statistics
Properties of Social Networks • “Small-world” phenomenon • Milgram asked participants to pass a letter to one of their close contacts in order to get it to an assigned individual • Most of the letters are lost (~75% of the letters) • The letters who reached their destination have passed through only about six people. • Origins of six degree • Mean geodesic distance l of graphs grows logarithmically or even slower with the network size. (dij is the shortest distance between node i and j) .
Properties of Social Networks • Degree DistributionClustering • Other important properties • Community Structure • Assortativity • Clustering Patterns • Homomiphly • …. • Many of these properties could be used for analyzing social networks.
Social Network Mining • Social network data is represented a graph • Individuals are represented as nodes • Nodes may have attributes to represent personal traits • Relationships are represented as edges • Edges may have attributes to represent relationship types • Edges may be directed • Common Social Network Mining tasks • Node classification • Link Prediction
Data Privacy Basics • How to share data without violating privacy? • Meaning of privacy? • Identity disclosure • Sensitive Attribute disclosure • Current techniques for structured data • K-anonymity • L-diversity • Secure multi-party computation • Problem: Publishing private data while, at the same time, protecting individual privacy • Challenges: • How to quantify privacy protection? • How to maximize the usefulness of published data? • How to minimize the risk of disclosure? • …
Sanitization and Anonymization Automated de-identification of private data with certain privacy guarantees Opposed to “formal determination by statisticians” requirement of HIPAA Two major research directions Perturbation (e.g. random noise addition) Anonymization (e.g. k-anonymization) Removing unique identifiers is not sufficient Quasi-identifier (QI) Maximal set of attributes that could help identify individuals Assumed to be publicly available (e.g., voter registration lists) As a process Remove all unique identifiers Identify QI-attributes, model adversary’s background knowledge Enforce some privacy definition (e.g. k-anonymity)
37 US states mandate collection of information She purchased the voter registration list for Cambridge Massachusetts 54,805 people 69% unique on postal code and birth date 87% US-wide with all three Solution:k-anonymity Any combination of values appears at least k times Developed systems that guarantee k-anonymity Minimize distortion of results Re-identifying “anonymous” data (Sweeney ’01)
k-Anonymity Each released record should be indistinguishable from at least (k-1) others on its QI attributes Alternatively: cardinality of any query result on released data should be at least k k-anonymity is (the first) one of many privacy definitions in this line of work l-diversity, t-closeness, m-invariance, delta-presence... Complementary Release Attack Different releases can be linked together to compromise k-anonymity. Solution: Consider all of the released tables before release the new one, and try to avoid linking. Other data holders may release some data that can be used in this kind of attack. Generally, this kind of attack is hard to be prohibited completely.
L-diversity principles L-diversity principle: A q-block is l-diverse if contains at least l ‘well represented” values for the sensitive attribute S. A table is l-diverse if every q-block is l-diverse l-diversity may be difficult and unnecessary to achieve. • A single sensitive attribute • Two values: HIV positive (1%) and HIV negative (99%) • Very different degrees of sensitivity • l-diversity is unnecessary to achieve • 2-diversity is unnecessary for an equivalence class that contains only negative records • l-diversity is difficult to achieve • Suppose there are 10000 records in total • To have distinct 2-diversity, there can be at most 10000*1%=100 equivalence classes
Privacy Preserving Distributed Data Mining • Goal of data mining is summary results • Association rules • Classifiers • Clusters • The results alone need not violate privacy • Contain no individually identifiable values • Reflect overall results, not individual organizations The problem is computing the results without access to the data! • Data needed for data mining maybe distributed among parties • Credit card fraud data • Inability to share data due to privacy reasons • HIPPAA • Even partial results may need to be kept private
Secure Multi-Party Computation (SMC) The goal is computing a function without revealing xi Semi-Honest Model Parties follow the protocol Malicious Model Parties may or may not follow the protocol We cannot do better then the existence of the third trusted party situation Generic SMC is too inefficient for PPDDM Enhancements being explored
Graph Model Lindamood et al. 09 & Heatherly et al. 09 • Graph represented by a set of homogenous vertices and a set of homogenous edges • Each node also has a set of Details, one of which is considered private.
Naïve Bayes Classification Lindamood et al. 09 & Heatherly et al. 09 • Classification based only on specified attributes in the node
Naïve Bayes with Links Lindamood et al. 09 & Heatherly et al. 09 • Rather than calculate the probability from person nx to ny we calculate the probability of a link from nxto a person with ny‘s traits
Link Weights Lindamood et al. 09 & Heatherly et al. 09 • Links also have associated weights • Represents how ‘close’ a friendship is suspected to be using the following formula:
Collective Inference Lindamood et al. 09 & Heatherly et al. 09 • Collection of techniques that use node attributes and the link structure to refine classifications. • Uses local classifiers to establish a set of priors for each node • Uses traditional relational classifiers as the iterative step in classification
Relational Classifiers Lindamood et al. 09 & Heatherly et al. 09 • Class Distribution Relational Neighbor • Weighted-Vote Relational Neighbor • Network-only Bayes Classifier • Network-only Link-based Classification
Experimental Data Lindamood et al. 09 & Heatherly et al. 09 • 167,000 profiles from the Facebook online social network • Restricted to public profiles in the Dallas/Fort Worth network • Over 3 million links
General Data Properties Lindamood et al. 09 & Heatherly et al. 09
Inference Methods Lindamood et al. 09 & Heatherly et al. 09 • Details only: Uses Naïve Bayes classifier to predict attribute • Links Only: Uses only the link structure to predict attribute • Average: Classifies based on an average of the probabilities computed by Details and Links
Predicting Private Details Lindamood et al. 09 & Heatherly et al. 09 • Attempt to predict the value of the political affiliation attribute • Three Inference Methods used as the local classifier • Relaxation labeling used as the Collective Inference method
Removing Details Lindamood et al. 09 & Heatherly et al. 09 • Ensures that no ‘false’ information is added to the network, all details in the released graph were entered by the user • Details that have the highest global probability of indicating political affiliation removed from the network
Removing Links Lindamood et al. 09 & Heatherly et al. 09 • Ensures that the link structure of the released graph is a subset of the original graph • Removes links from each node that are the most like the current node
Most Liberal Traits Lindamood et al. 09 & Heatherly et al. 09
Most Conservative Traits Lindamood et al. 09 & Heatherly et al. 09
Most Liberal Traits per Trait Name Lindamood et al. 09 & Heatherly et al. 09
Experiments Lindamood et al. 09 & Heatherly et al. 09 • Conducted on 35,000 nodes which recorded political affiliation • Tests removing 0 details and 0 links, 10 details and 0 links, 0 details and 10 links, and 10 details and 10 links • Varied Training Set size from 10% of available nodes to 90%
Local Classifier Results Lindamood et al. 09 & Heatherly et al. 09
Collective Inference Results Lindamood et al. 09 & Heatherly et al. 09
Online Social Networks Access Control Issues • Current access control systems for online social networks are either too restrictive or too loose • “selected friends” • Bebo, Facebook, and Multiply. • “neighbors” (i.e., the set of users having musical preferences and tastes similar to mine) • Last.fm • “friends of friends” • (Facebook, Friendster, Orkut); • “contacts of my contacts” (2nd degree contacts), “3rd” and“4th degree contacts” • Xing
Challenges I want only my family and close friends to see this picture.
Requirements • Many different online social networks with different terminology • Facebook vs Linkedin • We need to have flexible models that can represent • User’s profiles • Relationships among users • (e.g. Bob is Alice’s close friend) • Resources • (e.g., online photo albums) • Relationships among users and resources • (e.g., Bob is the owner of the photo album and Alice is tagged in this photo), • Actions (e.g., post a message on someone’s wall).
Overview of the Solution • We use semantic web technologies (e.g., OWL) to represent social network knowledge base. • We use semantic web rule language (SWRL) to represent various security, admin and filter policies.
Modeling User Profiles and Resources • Existing ontologies such as FoAF could be extended to capture user profiles. • Relationship among resources could be captured by using OWL concepts • PhotoAlbum rdfs:subClassOf Resource • PhotoAlbum consistsOf Photos
Modeling Relationships Among Users • We model relationships among users by defining N-ary relationship • :Christine a :Person ; :has_friend _:Friendship_Relation_1. :_Friendship_relation_1 a :Friendship_Relation ; :Friendship_trust :HIGH; :Friendship_value :Mike . • Owl reasoners cannot be used to infer some relationships such as Christine is a third degree friend of John. • Such computations needs to be done separately and represented by using new class.
Specifying Policies Using OSN Knowledge Base • Most of the OSN information could be captured using OWL to represent rich set of concepts • This makes it possible to specify very flexible access control policies • “Photos could be accessed by friends only” automatically implies closeFriend can access the photos too. • Policies could be defined based on user-resource relationships easily.
Security Policies for OSNs • Access control policies • Filtering policies • Could be specified by user • Could be specified by authorized user • Admin policies • Security admin specifies who is authorized specify filtering and access control policies • Exp: if U1 isParentOf U2 and U2 is a child than U1 can specify filtering policies for U2.
Security Policy Specification (using semantic web technologies) • Semantic Web Rule Language (SWRL) is used for specifying access control, filtering and authorization policies. • SWRL is based on OWL: • all rules are expressed in terms of OWL concepts (classes, properties, individuals, literals…). • Using SWRL, subject, object and actions are specified • Rules can have different authorization that states the subject’s rights on target object.
Knowledge based for Authorizations and Prohibitions • Authorizations/Prohibitions needs to be specified using OWL • Different object property for each actions supported by OSN. • Authorizations/prohibitions could automatically propagate based on action hierarchies • Assume “post” is a subproperty of “write” • If a user is given “post” permission than user will have “write” permission as well • Admin Prohibitions need to be specified slightly different. (Supervisor, Target, Object, Privilige)
Security Rule Examples • SWRL rule specification does depend on the authorization and OSN knowledge bases. • It is not possible to specify generic rules • Examples:
Security Rule Enforcement • A reference monitor evaluates the requests. • Admin request for access control could be evaluated by rule rewriting • Example: Assume Bob submits the following admin request • Rewrite as the following rule
Security Rule Enforcement • Admin requests for Prohibitions could be rewritten as well. • Example: Bob issues the following prohibition request • Rewritten version • Access control requests needs to consider both filter and access control policies
Framework Architecture Social Network Application Access Decision Access request SN Knowledge Base Reference Monitor Knowledge Base Queries Modified Access request Reasoning Result Semantic Web Reasoning Engine Policy Store Policy Retrieval
Conclusions • Various attacks exist to • Identify nodes in anonymized data • Infer private details • Recent attempts to increase social network access control to limit some of the attacks • Balancing privacy, security and usability on online social networks will be an important challenge • Directions • Scalability • We are currently implementing such system to test its scalability. • Usability • Create techniques to automatically learn rules • Create simple user interfaces so that users can easily specify these rules.