title: Privacy-preserving heterogeneous health data sharing.
abstract: OBJECTIVE
  Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful information. Among existing privacy models, ε-differential privacy provides one of the strongest privacy guarantees and makes no assumptions about an adversary's background knowledge. All existing solutions that ensure ε-differential privacy handle the problem of disclosing relational and set-valued data in a privacy-preserving manner separately. In this paper, we propose an algorithm that considers both relational and set-valued data in differentially private disclosure of healthcare data.
  
  
  METHODS
  The proposed approach makes a simple yet fundamental switch in differentially private algorithm design: instead of listing all possible records (ie, a contingency table) for noise addition, records are generalized before noise addition. The algorithm first generalizes the raw data in a probabilistic way, and then adds noise to guarantee ε-differential privacy.
  
  
  RESULTS
  We showed that the disclosed data could be used effectively to build a decision tree induction classifier. Experimental results demonstrated that the proposed algorithm is scalable and performs better than existing solutions for classification analysis.
  
  
  CONCLUSIONS
  The resulting utility may degrade when the output domain size is very large, making it potentially inappropriate to generate synthetic data for large health databases.
  
  
  CONCLUSIONS
  Unlike existing techniques, the proposed algorithm allows the disclosure of health data containing both relational and set-valued data in a differentially private manner, and can retain essential information for discriminative analysis.
authors.affiliation: Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada. no_moham@cse.concordia.ca
authors.forename: Noman
authors.initials: N
authors.lastname: Mohammed
authors.forename: Xiaoqian
authors.initials: X
authors.lastname: Jiang
authors.forename: Rui
authors.initials: R
authors.lastname: Chen
authors.forename: Benjamin C M
authors.initials: BC
authors.lastname: Fung
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
mesh_terms: D000465:Algorithms; D003219:Confidentiality; D057225:Data Mining; D016208:Databases, Factual; D005260:Female; D006801:Humans; D033181:Information Dissemination; D008297:Male; D018907:Privacy