423
Views
5
CrossRef citations to date
0
Altmetric
Articles

HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets

ORCID Icon, , , , & ORCID Icon
Pages 249-271 | Received 01 Aug 2018, Accepted 03 Nov 2018, Published online: 11 Nov 2018

References

  • Donoho D. 50 years of data science. J Comput Graph Stat. 2017;26(4):745–766. doi: 10.1080/10618600.2017.1384734
  • Golle P. Revisiting the uniqueness of simple demographics in the US population. Proceedings of the 5th ACM Workshop on Privacy in Electronic Society. ACM; 2006.
  • Sweeney L. Weaving technology and policy together to maintain confidentiality. J Law Med Ethics. 1997;25(2–3):98–110. doi: 10.1111/j.1748-720X.1997.tb01885.x
  • Sweeney L. Simple demographics often identify people uniquely. Health (San Francisco). 2000;671:1–34.
  • Aggarwal G, et al. Approximation algorithms for k-anonymity. J Privacy Technol. 2005:1–18. http://ilpubs.stanford.edu:8090/645/1/2004-24.pdf.
  • Harper FM, Konstan JA. The movielens datasets: history and context. ACM Trans Interact Intell Syst. 2016;5(4):19.
  • Dwork C, Roth A. The algorithmic foundations of differential privacy. Found Trends Theoret Comput Sci. 2014;9(3–4):211–407. doi: 10.1561/0400000042
  • Dwork C. Differential privacy: a survey of results. International Conference on Theory and Applications of Models of Computation. Springer; 2008.
  • Dinur I, Nissim K. Revealing information while preserving privacy. Proceedings of the Twenty-second ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. ACM; 2003.
  • Dwork C, et al. Calibrating noise to sensitivity in private data analysis. Theory of Cryptography Conference. Springer; 2006.
  • Mohammed N, et al. Differentially private data release for data mining. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; 2011.
  • Raskhodnikova S, et al. What can we learn privately. Proceedings of the 54th Annual Symposium on Foundations of Computer Science. 2008.
  • Zhang J, et al. Privbayes: private data release via Bayesian networks. ACM Trans Database Syst. 2017;42(4):25. doi: 10.1145/3134428
  • Chen R, et al. Differentially private high-dimensional data publication via sampling-based inference. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM; 2015.
  • Bhanot R, Hans R. A review and comparative analysis of various encryption algorithms. Int J Secur Appl. 2015;9(4):289–306.
  • Stallings W, et al. Computer security principles and practice. Upper Saddle River (NJ): Pearson Education; 2012.
  • Suo H, et al. Security in the internet of things: a review. 2012 International Conference on Computer Science and Electronics Engineering (ICCSEE). IEEE; 2012.
  • Gentry C. A fully homomorphic encryption scheme. Palo Alto (CA): Stanford University; 2009.
  • Gentry C, Halevi S. Implementing gentry’s fully-homomorphic encryption scheme. Annual International Conference on the Theory and Applications of Cryptographic Techniques. Springer; 2011.
  • Gentry C, Sahai A, Waters B. Homomorphic encryption from learning with errors: conceptually-simpler, asymptotically-faster, attribute-basedAdvances in cryptology – CRYPTO 2013. Santa Barbara (CA): Springer; 2013. p. 75–92.
  • Van Dijk M, et al. Fully homomorphic encryption over the integers. Annual International Conference on the Theory and Applications of Cryptographic Techniques. Springer; 2010.
  • Little RJ. A test of missing completely at random for multivariate data with missing values. J Am Stat Assoc. 1988;83(404):1198–1202. doi: 10.1080/01621459.1988.10478722
  • Stekhoven DJ, Bühlmann P. Missforest—non-parametric missing value imputation for mixed-type data. Bioinformatics. 2011;28(1):112–118. doi: 10.1093/bioinformatics/btr597
  • Gower JC. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika. 1966;53(3–4):325–338. doi: 10.1093/biomet/53.3-4.325
  • Gower JC. Properties of Euclidean and non-Euclidean distance matrices. Linear Algebra Appl. 1985;67:81–97. doi: 10.1016/0024-3795(85)90187-9
  • Haggag MM. Adjusting the penalized term for the regularized regression models. Afr Stat. 2018;13(2):1609–1630.
  • Di Martino A, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry. 2014;19(6):659. doi: 10.1038/mp.2013.78
  • Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2(3):18–22.
  • Wilkinson MD, et al. The FAIR guiding principles for scientific data management and stewardship. Sci Data. 2016;3:1–9. doi: 10.1038/sdata.2016.18

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.