TY - GEN
T1 - A second order cone formulation of continuous CTA model
AU - Lesaja, Goran
AU - Castro, Jordi
AU - Oganian, Anna
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - 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 ℓ1 or ℓ2 norm; with each measure having its advantages and disadvantages. Recently, in [4] a regularization of the ℓ1-CTA using Pseudo-Huber function was introduced in an attempt to combine positive characteristics of both ℓ1-CTA and ℓ2-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 ℓ1-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.
AB - 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 ℓ1 or ℓ2 norm; with each measure having its advantages and disadvantages. Recently, in [4] a regularization of the ℓ1-CTA using Pseudo-Huber function was introduced in an attempt to combine positive characteristics of both ℓ1-CTA and ℓ2-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 ℓ1-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.
KW - Controlled tabular adjustment models
KW - Convex optimization
KW - Interior-point methods
KW - Pseudo-huber function
KW - Second-order cone optimization
KW - Statistical disclosure limitation (control)
UR - http://www.scopus.com/inward/record.url?scp=84987935082&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-45381-1_4
DO - 10.1007/978-3-319-45381-1_4
M3 - Conference article
AN - SCOPUS:84987935082
SN - 9783319453804
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 53
BT - Privacy in Statistical Databases - UNESCO Chair in Data Privacy International Conference, PSD 2016, Proceedings
A2 - Domingo-Ferrer, Josep
A2 - Pejić-Bach, Mirjana
PB - Springer Verlag
T2 - International Conference on Privacy in Statistical Databases, PSD 2016
Y2 - 14 September 2016 through 16 September 2016
ER -