title: A globally optimal k-anonymity method for the de-identification of health data.
abstract: BACKGROUND
  Explicit patient consent requirements in privacy laws can have a negative impact on health research, leading to selection bias and reduced recruitment. Often legislative requirements to obtain consent are waived if the information collected or disclosed is de-identified.
  
  
  OBJECTIVE
  The authors developed and empirically evaluated a new globally optimal de-identification algorithm that satisfies the k-anonymity criterion and that is suitable for health datasets.
  
  
  METHODS
  Authors compared OLA (Optimal Lattice Anonymization) empirically to three existing k-anonymity algorithms, Datafly, Samarati, and Incognito, on six public, hospital, and registry datasets for different values of k and suppression limits. Measurement Three information loss metrics were used for the comparison: precision, discernability metric, and non-uniform entropy. Each algorithm's performance speed was also evaluated.
  
  
  RESULTS
  The Datafly and Samarati algorithms had higher information loss than OLA and Incognito; OLA was consistently faster than Incognito in finding the globally optimal de-identification solution.
  
  
  CONCLUSIONS
  For the de-identification of health datasets, OLA is an improvement on existing k-anonymity algorithms in terms of information loss and performance.
authors: CHEO Research Institute, 401 Smyth Road, Ott, ON K1H8L1, Canada. kelemam@uottawa.ca
authors: Khaled
authors: K
authors: El Emam
authors: Fida Kamal
authors: FK
authors: Dankar
authors: Romeo
authors: R
authors: Issa
authors: Elizabeth
authors: E
authors: Jonker
authors: Daniel
authors: D
authors: Amyot
authors: Elise
authors: E
authors: Cogo
authors: Jean-Pierre
authors: JP
authors: Corriveau
authors: Mark
authors: M
authors: Walker
authors: Sadrul
authors: S
authors: Chowdhury
authors: Regis
authors: R
authors: Vaillancourt
authors: Tyson
authors: T
authors: Roffey
authors: Jim
authors: J
authors: Bottomley
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D003219:Confidentiality; D005260:Female; D006801:Humans; D016247:Information Storage and Retrieval; D008297:Male; D016347:Medical Records Systems, Computerized