Privacy

Privacy

  • Determine when/how/what information is communicated to others
  • Aspects
  • Right to be let alone
  • Self-identity / personal growth
  • Protection intimate relationships
  • Data privacy: Detect/control data inference
  • DB Security: Control access / protect content
  • Challenge: Utilize data while protect individuals privacy
  • Idea: Anonymize all attributes that could be used for de-anonymization

Privacy: Risks + Protection

  • Problem: Growing availability of information
  • Who is allowed to see what?
  • Goal: Anonymize data
  • Protection technique: Formal models
  • Specify privacy model
  • Derive conditions
  • Design algorithms satisfying privacy conditions
  • Example: K-anonymity, L-diversity, L-closeness, (c,k)-safety

k-Anonymity

  • Protection model, ensure anonymity in relational data
  • Idea: Generalize, modify, distort identifiers -> no unique identification
  • Definition: R(A1, … An), Quasi-identifiers
  • R satisfies k-Anonymity <=> each sequence R[QI] at least k times
  • Meaning: Matching cannot reduce to fewer than k individuals
  • Large k => higher privacy
  • Find quasi-identifiers: Hard / impossible
  • Algorithm (NP-hard)
  • Delete cell values / tuples (generalization causes information loss)
  • Generalize cell values (quasi-identifiers)

Attacks

  • Unsorted matching attack:
  • Data often sorted, sort data
  • Complementary release attack:
  • Two data sets, match with problem attribute
  • Homogeneity attack:
  • Problem attribute for class is same (little diversity)
  • Background knowledge attack:
  • Very complex attack (knowledge not known)

L-Diversity

  • Prevent homogeneity / background knowledge attack
  • Sensitive attributes in a class must be L different values
  • No perfect anonymization (Binary attribute hard to achieve, distribution not considered)

Curse of Dimensionality

  • Data many attributes
  • Data sparse
  • K nearest neighbors are far away
  • Result: Loss of information

Sparse data: Deanonymization

  • no quasi-identifiers
  • Netflix: K-Anonymization generalization high data loss
  • Idea:
  • Calculate similarity Netflix and IMDB
  • Most similar record is a match
  • No record: Candidates also reveal information
  • Information:
  • political orientation
  • Religious views

Social Networks

  • Prediction of intimate information (sexual orientation, ethnicity, religious)
  • Interaction tells sth about user
  • Goals anonymization
  • No identification individuals
  • No inference of friendships/interaction
  • Secret sensitive attributes
  • Re-identification: Graph + background knowledge

Social Networks: Advanced anonymization

  • K-anonymization? No changing of neighborhood
  • Protect:
  • node re-identification
  • Edge disclosure
  • Sensitive attributes
  • Anonymize
  • Node degree
  • Neighborhood
  • Attributes
  • Structural knowledge
  • Degree distribution
  • Goal: Transform to k-degree anonymous graph
  • Create degree distribution similar to original one (edges)