PRIVACY AND TRUST IN SOCIAL NETWORK

PRIVACY AND TRUSTIN SOCIAL NETWORK Michelle Hong2009/03/02

Outline • What is privacy and trust? • Privacy in social network • Basic privacy requirement • Privacy in graph • Trust in social network • Reference

What is Privacy • Privacy is the ability of an individual or group to seclude themselves or information about themselves and thereby reveal themselves selectively. • Different privacy boundaries and content • Voluntarily sacrificed • Uniquely identifiable data relating to a person or persons

What is Trust? • Trust is a relationship of reliance. • Not related to good character, or morals • Trust does not need to include an action that you and the other party are mutually engaged in. • Trust is a prediction of reliance on an action. • Conditional

Privacy and Trust Tradeoff Privacy Trust • Need legal rights • Reveal more data to trustworthy people • Provide access rights • Gain trust through open sensitive data

K-anonymous [1] • Given multiple data publisher • Get sensitive value Have at least k answers

L-diversity [2] Have at least l different sensitive answers

t-closeness [3] T semantic meaning result

DynamicAnonymization [4]

Possible Attacks On Anonymized Graphs • Attack method [5] • Identify by neighborhood information • It includes: • Vertex Refinement Queries • Sub-graph Queries • Hub Fingerprint Queries • Attack types[6] • Active Attacks • Create a small number of new user accounts linking with other users before the anonymized graph is generated • Passive Attacks • Indentify themselves in the published graph • Semi-passive Attacks • Create necessary link with other users

Vertex Refinement Queries H*’s computation is linear in the number of edges in the graph, very efficiently.

Sub-graph Queries Query is the subgraph information adjacent to the target node Computation intensive

Hub Fingerprint Queries Suppose Dave and Ed are selected as hubs F1(fred) = (1, 0) (The shortest path length to each hub) F2(fred) = (1, 2) If F1(fred) = (1, 0) in open world, then both F1(fred) = (1, 0) and (1, 1) are candidate because the adversary may not have the complete knowledge

Avoid attacks • Request authorities to linkage confirmation • Users confirm a request about adding a friend • Website provides checking on users • Identify and remove attack nodes • Find the strange structure nodes

k-degree anonymous[7] • The kind of attack • Vertex Refinement Queries (H(1)) • Objective • The published graph • For every node v, there exist at least k-1 other nodes in the graph with the same degree as v • Minimum edges are added in to reserve the graph’s shape as much as possible • Method • Add edges into the original anonymized graph • First compute the new degree vector that satisfy k-degree • Then generate the new graph based on this degree vector

K-neighbor anonymous [8]

Resist neighborhood attack through graph generalization[5] Step1: Partition the graph, each partition contains at least k nodes 2 1 3 1 2 2 2 Step2: For each partition, generate a super node 2 3 3 2 Step3: Draw the edges between partitions, the weight is the edge number In this paper, he use simulated annealing to find the partitions maximize the likelihood function Step3: Draw the sel-edges for each partition, the weight is the edge number with it

Mining Privacy in Social Network • What’s the problem in Web 2.0: • Activity streams: users are not aware that some mini-feeds on the profile • Unwelcome linkage: a friend who explicitly write the link for other user's profile • merge social graph: link of link

Privacy in Social Data • Different users have different opinions on sensitive data • Website enables users to set up access permission • Construct trust network from social data

Reference [1] L. Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 557-570. [2] Ashwin Machanavajjhala , Daniel Kifer , Johannes Gehrke , Muthuramakrishnan Venkitasubramaniam, L-diversity: Privacy beyond k-anonymity, ACM Transactions on Knowledge Discovery from Data (TKDD), v.1 n.1, p.3-es, March 2007 [3] Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian, "t-Closeness: Privacy Beyond k-Anonymity and l-Diversity," in IEEE International Conference on Data Engineering (this proceedings), 2007. [4] Xiao, X., Tao, Y. Dynamic Anonymization: Accurate Statistical Analysis with Privacy Preservation. Proceedings of ACM Conference on Management of Data (SIGMOD), pages 107-120, 2008. [5] Michael Hay, Gerome Miklau, David Jensen, Don Towsley and Philipp Weis, Resisting Structural Re-identification in Anonymized Social Networks. PVLDB08 [6] Lars Backstrom, Cynthia Dwork and Jon Kleinberg, Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and Structural Steganography. WWW2007 [7] Kun liu and Evimaria Terzi, Towards Identity Anonymization on Graphs. SIGMOD08 [8] Bin Zhou and Jian Pei, Preserving Privacy in Social Networks Against Neighborhood Attacks ICDE08

PRIVACY AND TRUST IN SOCIAL NETWORK