Name
Abstract | ||
|---|---|---|
In this paper we consider a minimum distance Controlled Tabular Adjustment (CTA) model for statistical disclosure limitation (control) of tabular data. The goal of the CTA model is to find the closest safe table to some original tabular data set that contains sensitive information. The measure of closeness is usually measured using l(1) or l(2) norm; with each measure having its advantages and disadvantages. Recently, in [4] a regularization of the l(1)-CTA using Pseudo-Huber function was introduced in an attempt to combine positive characteristics of both l(1)-CTA and l(2)-CTA. All three models can be solved using appropriate versions of Interior-Point Methods (IPM). It is known that IPM in general works better on well structured problems such as conic optimization problems, thus, reformulation of these CTA models as conic optimization problem may be advantageous. We present reformulation of Pseudo-Huber-CTA, and l(1)-CTA as Second-Order Cone (SOC) optimization problems and test the validity of the approach on the small example of two-dimensional tabular data set. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-45381-1_4 | PRIVACY IN STATISTICAL DATABASES: UNESCO CHAIR IN DATA PRIVACY |
Keywords | Field | DocType |
Statistical disclosure limitation (control), Controlled tabular adjustment models, Pseudo-Huber Function, Convex optimization, Second-order cone optimization, Interior-Point Methods | Second-order cone programming,Data mining,Discrete mathematics,Regularization (mathematics),Statistical disclosure limitation,Conic optimization,Interior point method,Convex optimization,Optimization problem,Mathematics,Calculus | Conference |
Volume | ISSN | Citations |
9867 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Goran Lesaja | 1 | 4 | 4.69 |
| Jordi Castro | 2 | 12 | 2.67 |
| Anna Oganian | 3 | 43 | 7.02 |