2,059 research outputs found
How to use data swapping to create useful dummy data for panel datasets
No full text"Many research data centres (RDCs) provide access to micro data by means of onsite use and remote execution of programs. An efficient usage of these modes of data access requires the researchers to have dummy data, which allows them to familiarize with the real data. These dummy data must be anonymous and look the same as the original data, but they do not have to render valid results. For complex datasets such as panel data or linked data, the creation of useful dummy data is not trivial. In this paper we suggest to use data swapping with constraints in order to keep some consistency and correlation between variables within crosssections and over time. It is easy to be implemented even for datasets with many variables and many survey waves." (Author's abstract, IAB-Doku) ((en))Prüfverfahren, Datenzugang, Datenaufbereitung - Test, IAB-Betriebspanel, Panel, IAB-Betriebs-Historik-Panel
Data swapping for protecting census tables
No full textThe pre-tabular statistical disclosure control (SDC) method of data swapping is the preferred method for protecting Census tabular data in some National Statistical Institutes, including the United States and Great Britain. A pre-tabular SDC method has the advantage that it only needs to be carried out once on the microdata and all tables released (under the conditions of the output strategies, eg. fixed categories of variables, minimum cell size and population thresholds) are considered protected. In this paper, we propose a method for targeted data swapping. The method involves a probability proportional to size selection strategy of high risk households for data swapping. The selected households are then paired with other households having the same control variables. In addition, the distance between paired households is determined by the level of risk with respect to the geographical hierarchies. The strategy is compared to a random data swapping strategy in terms of the disclosure risk and data utility
Statistical disclosure control methods for census frequency tables
No full textThis paper provides a review of common statistical disclosure control (SDC) methods implemented at Statistical Agencies for standard tabular outputs containing whole population counts from a Census (either enumerated or based on a register). These methods include record swapping on the microdata prior to its tabulation and rounding of entries in the tables after they are produced. The approach for assessing SDC methods is based on a disclosure risk–data utility framework and the need to find the balance between managing disclosure risk while maximizing the amount of information that can be released to users and ensuring high quality outputs. To carry out the analysis, quantitative measures of disclosure risk and data utility are defined and methods compared. Conclusions from the analysis show that record swapping as a sole SDC method leaves high probabilities of disclosure risk. Targeted record swapping lowers the disclosure risk, but there is more distortion to distributions. Small cell adjustments (rounding) give protection to Census tables by eliminating small cells but only one set of variables and geographies can be disseminated in order to avoid disclosure by differencing nested tables. Full random rounding offers more protection against disclosure by differencing, but margins are typically rounded separately from the internal cells and tables are not additive. Rounding procedures protect against the perception of disclosure risk compared to record swapping since no small cells appear in the tables. Combining rounding with record swapping raises the level of protection but increases the loss of utility to Census tabular outputs. For some statistical analysis, the combination of record swapping and rounding balances to some degree opposing effects that the methods have on the utility of the tables
Assessing the disclosure protection provided by misclassification for survey microdata
No full textGovernment statistical agencies often apply statistical disclosure limitation techniques to survey microdata to protect confidentiality. There is a need for ways to assess the protection provided. This paper develops some simple methods for disclosure limitation techniques which perturb the values of categorical identifying variables. The methods are applied in numerical experiments based upon census data from the United Kingdom which are subject to two perturbation techniques: data swapping and the post randomisation method. Some simplifying approximations to the measure of risk are found to work well in capturing the impacts of these techniques. These approximations provide simple extensions of existing risk assessment methods based upon Poisson log-linear models. A numerical experiment is also undertaken to assess the impact of multivariate misclassification with an increasing number of identifying variables. The methods developed in this paper may also be used to obtain more realistic assessments of risk which take account of the kinds of measurement and other non-sampling errors commonly arising in surveys
NISS WebSwap: A Web Service for Data Swapping
No full textData swapping is a statistical disclosure limitation practice that alters records in the data to be released by switching values of attributes across pairs of records in a fraction of the original data. Web Services are an exciting new form of distributed computing that allow users to invoke remote applications nearly transparently. National Institute of Statistical Sciences (NISS) has recently started hosting NISS Web Services as a service and example to the statistical sciences community. In this paper we describe and provide usage information for NISS WebSwap the initial NISS Web Service, which swaps one or more attributes (fields) between user-specified records in a microdata file, uploading the original data file from the user's computer and downloading the file containing the swapped records.
ChargeUp! Data Swap: Using data from battery swapping e-motorcycles in Nairobi to assess impacts and plan infrastructure
No full textThe dearth of available data on e-motorcycle usage in African
cities is a significant challenge in impact studies of e-motorcycle
deployment. The ChargeUp! project aimed to fill this research gap
using operational data from e-motorcycles and battery swap stations
in Nairobi to perform modelling and analysis to determine several
key outputs. This project included the analysis of: e-motorcycle trips;
battery swapping demand; battery charging energy consumption;
swap battery charging related emissions for a high renewables and
high fossil energy mix scenarios; charging related electricity costs
for different tariff scenarios; the effect of a co-ordinated charging
scenario on emissions and tariffs; optimal battery ratios and required
numbers of swap stations; and a methodology to determine optimal
regions for battery swap stations based on trip data
Multivariate equi-width data swapping for private data publication
No full textAlso published in: Advances in knowledge discovery and data mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010: Proceedings, Part I / Mohammed J. Zaki, Jeffrey Xu Yu, B. Ravindran and Vikram Pudi (eds.), pp. 208-215In many privacy preserving applications, specific variables are required to be disturbed simultaneously in order to guarantee correlations among them. Multivariate Equi-Depth Swapping (MEDS) is a natural solution in such cases, since it provides uniform privacy protection for each data tuple. However, this approach performs ineffectively not only in computational complexity (basically O(n 3) for n data tuples), but in data utility for distance-based data analysis. This paper discusses the utilisation of Multivariate Equi-Width Swapping (MEWS) to enhance the utility preservation for such cases. With extensive theoretical analysis and experimental results, we show that, MEWS can achieve a similar performance in privacy preservation to that of MEDS and has only O(n) computational complexity.Yidong Li and Hong She
A Risk-Utility Framework for
No full textData swapping is a statistical disclosure limitation method used to protect the confidentiality of data by interchanging variable values between records. We propose a risk-utility framework for selecting an optimal swapped data release when considering several swap variables and multiple swap rates. Risk and utility values associated with each such swapped data file are traded off along a frontier of undominated potential releases, which contains the optimal release(s). Current Population Survey data are used to illustrate the framework for categorical data swapping
Equi-width data swapping for private data publication
No full textData Swapping is a popular value-invariant data perturbation technique. The quality of a data swapping method is measured by how well it preserves data privacy and data utility. As swapping data globally is computationally impractical, to guarantee its performance in these metrics appropriate, localization schemes are often conducted in advance. Equi-depth partitioning is preferred by most of the existing data perturbation techniques as it provides uniform privacy protection for each data tuple. However, this method performs ineffectively for two types of applications: one is to maintain statistics based on equi-width partitioning, such as the multivariate histogram with equal bin width, and the other is to preserve parametric statistics, such as covariance, in the context of sparse data with non-uniform distribution. As a natural solution for the above application, this paper explores the possibility of using data swapping with equi-width partitioning for private data publication, which has been little used in data perturbation due to the difficulty of preserving data privacy. With extensive theoretical analysis and experimental results, we show that, Equi-Width Swapping (EWS)can achieve a similar performance in privacy preservation to that of Equi-Depth Swapping (EDS) if the number of partitions is sufficiently large (e. g. à ¿ = à ¿N, where N is the size of dataset). Our experimental results in both synthetic and real-world data validate our theoretical analysis.Yidong Li and Hong She
Releasing microdata: disclosure risk estimation, data masking and assessing utility
No full textStatistical Agencies need to make informed decisions when releasing sample microdata from social surveys with respect to the level of protection required in the data and the mode of access. These decisions should be based on objective quantitative measures of disclosure risk and data utility. This paper reviews recent developments in disclosure risk assessment and discusses how these can be integrated with established methods of data masking and utility assessment for releasing microdata. We illustrate the Disclosure risk-Data Utility approach based on samples drawn from a Census where the population is known and can be used to investigate sample-based methods and validate results
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