Secure computation with horizontally partitioned data using adaptive regression splines

Authors:
Joyee Ghosh;Jerome P. Reiter;Alan F. Karr
Affiliations:
Duke University, Durham, NC, USA;Duke University, Durham, NC, USA;National Institute of Statistical Sciences, Research Triangle Park, NC, USA
Venue:
Computational Statistics & Data Analysis
Year:
2007

Citing 7
Cited 2

Secret sharing homomorphisms: keeping shares of a secret secret

Proceedings on Advances in cryptology---CRYPTO '86
Privacy-preserving data mining

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Privacy preserving association rule mining in vertically partitioned data

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Model Diagnostics for Remote Access Regression Servers

Statistics and Computing
Privacy-preserving k-means clustering over vertically partitioned data

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy preserving mining of association rules

Information Systems - Knowledge discovery and data mining (KDD 2002)
Privacy-preserving clustering with distributed EM mixture modeling

Knowledge and Information Systems

Importance partitioning in micro-aggregation

Computational Statistics & Data Analysis
Secure Bayesian model averaging for horizontally partitioned data

Statistics and Computing

Quantified Score

Hi-index	0.03

Visualization

Abstract

When several data owners possess data on different records but the same variables, known as horizontally partitioned data, the owners can improve statistical inferences by sharing their data with each other. Often, however, the owners are unwilling or unable to share because the data are confidential or proprietary. Secure computation protocols enable the owners to compute parameter estimates for some statistical models, including linear regressions, without sharing individual records' data. A drawback to these techniques is that the model must be specified in advance of initiating the protocol, and the usual exploratory strategies for determining good-fitting models have limited usefulness since the individual records are not shared. In this paper, we present a protocol for secure adaptive regression splines that allows for flexible, semi-automatic regression modeling. This reduces the risk of model mis-specification inherent in secure computation settings. We illustrate the protocol with air pollution data.