Knowledge Management Semantic Web and Social Networking Security and Privacy in Online Social NetworksOutlineSocial NetworksEnron Social Graph*Romantic Relations at “Jefferson High School”Emergence of Online Social NetworksProperties of Social Networks“Small-World” Example: Six Degrees of Kevin BaconSlide 9Social Network MiningData Privacy BasicsSanitization and AnonymizationRe-identifying “anonymous” data (Sweeney ’01)k-AnonymityL-diversity principlesPrivacy Preserving Distributed Data MiningSecure Multi-Party Computation (SMC)Graph ModelNaïve Bayes ClassificationNaïve Bayes with LinksLink WeightsCollective InferenceRelational ClassifiersExperimental DataGeneral Data PropertiesInference MethodsPredicting Private DetailsRemoving DetailsRemoving LinksMost Liberal TraitsMost Conservative TraitsMost Liberal Traits per Trait NameExperimentsLocal Classifier ResultsCollective Inference ResultsOnline Social Networks Access Control IssuesChallengesRequirementsOverview of the SolutionModeling User Profiles and ResourcesModeling Relationships Among UsersSpecifying Policies Using OSN Knowledge BaseSecurity Policies for OSNsSecurity Policy Specification (using semantic web technologies)Knowledge based for Authorizations and ProhibitionsSecurity Rule ExamplesSecurity Rule EnforcementSlide 50Framework ArchitectureConclusionsUT DALLASUT DALLASErik Jonsson School of Engineering & Computer ScienceFEARLESS engineeringKnowledge ManagementSemantic Web and Social NetworkingSecurity and Privacy in Online Social NetworksMurat KantarciogluBhavani ThuraisinghamThanks to Raymond Heatherly and Barbara Carminati for helping in slide preparationsFEARLESS engineeringOutline•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 NetworksFEARLESS engineeringSocial 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 analysisFEARLESS engineeringEnron Social Graph** http://jheer.org/enron/FEARLESS engineeringRomantic Relations at “Jefferson High School”FEARLESS engineeringEmergence 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?statisticsFEARLESS engineeringProperties 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) . jiijdnnl)1(2FEARLESS engineering“Small-World” Example: Six Degrees of Kevin BaconFEARLESS engineeringProperties 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.FEARLESS engineeringSocial 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 PredictionFEARLESS engineeringData 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?–…FEARLESS engineeringSanitization and Anonymization•Automated de-identification of private data with certain privacy guarantees–Opposed to “formal determination by statisticians” requirement of HIPAA•Two major research directions1. Perturbation (e.g. random noise addition)2. 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 process1. Remove all unique identifiers2. Identify QI-attributes, model adversary’s background knowledge3. Enforce some privacy definition (e.g. k-anonymity)FEARLESS engineeringRe-identifying “anonymous” data (Sweeney ’01)•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 resultsFEARLESS engineeringk-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.FEARLESS engineeringL-diversity principles•L-diversity principle: A q-block is l-diverse if contains at least
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