A Fast Minimal Infrequent Itemset Mining Algorithm

A novel fast algorithm for finding quasi identifiers in large datasets is presented. Performance measurements on a broad range of datasets demonstrate substantial reductions in run-time relative to the state of the art and the scalability of the algorithm to realistically-sized datasets up to several million records

A Fast Minimal Infrequent Itemset Mining Algorithm

A novel fast algorithm for finding quasi identifiers in large datasets is presented. Performance measurements on a broad range of datasets demonstrate substantial reductions in run-time relative to the state of the art and the scalability of the algorithm to realistically-sized datasets up to several million records

Generalization-Based k-Anonymization

Microaggregation is an anonymization technique consisting on partitioning the data into clusters no smaller than k elements and then replacing the whole cluster by its prototypical representant. Most of microaggregation techniques work on numerical attributes. However, many data sets are described by heterogeneous types of data, i.e., nu- merical and categorical attributes. In this paper we propose a new mi- croaggregation method for achieving a compliant k-anonymous masked file for categorical microdata based on generalization. The goal is to build a generalized description satisfied by at least k domain objects and to replace these domain objects by the description. The way to construct that generalization is similar that the one used in growing decision trees. Records that cannot be generalized satisfactorily are discarded, therefore some information is lost. In the experiments we performed we prove that the new approach gives good results. © Springer International Publishing Switzerland 2015.This research is partially funded by the Spanish MICINN projects COGNITIO (TIN-2012-38450-C03-03), EdeTRI (TIN2012-39348-C02-01) and COPRIVACY (TIN2011-27076-C03-03), the grant 2009-SGR-1434 from the Generalitat de Catalunya, and the European Project DwB (Grant Agreement Number 262608)Peer reviewe

Real Time Econometrics

This paper considers the problems facing decision-makers using econometric models in real time. It identifies the key stages involved and highlights the role of automated systems in reducing the effect of data snooping. It sets out many choices that researchers face in construction of automated systems and discusses some of the possible ways advanced in the literature for dealing with them. The role of feedbacks from the decision-maker’s actions to the data generating process is also discussed and highlighted through an example.specification search, data snooping, recursive/sequential modelling, automated model selection

Mathematically optimized, recursive prepartitioning strategies for k-anonymous microaggregation of large-scale datasets

© Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The technical contents of this work fall within the statistical disclosure control (SDC) field, which concerns the postprocessing of the demographic portion of the statistical results of surveys containing sensitive personal information, in order to effectively safeguard the anonymity of the participating respondents. A widely known technique to solve the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers is the k-anonymous microaggregation. Unfortunately, most microaggregation algorithms that produce competitively low levels of distortions exhibit a superlinear running time, typically scaling with the square of the number of records in the dataset. This work proposes and analyzes an optimized prepartitioning strategy to reduce significantly the running time for the k-anonymous microaggregation algorithm operating on large datasets, with mild loss in data utility with respect to that of MDAV, the underlying method. The optimization strategy is based on prepartitioning a dataset recursively until the desired k-anonymity parameter is achieved. Traditional microaggregation algorithms have quadratic computational complexity in the form T(n2). By using the proposed method and fixing the number of recurrent prepartitions we obtain subquadratic complexity in the form T(n3/2), T(n4/3), ..., depending on the number of prepartitions. Alternatively, fixing the ratio between the size of the microcell and the macrocell on each prepartition, quasilinear complexity in the form T(nlog¿n) is achieved. Our method is readily applicable to large-scale datasets with numerical demographic attributes.Peer ReviewedPostprint (author's final draft

A Novel Privacy Disclosure Risk Measure and Optimizing Privacy Preserving Data Publishing Techniques

A tremendous amount of individual-level data is generated each day, with a wide variety of uses. This data often contains sensitive information about individuals, which can be disclosed by “adversaries”. Even when direct identifiers such as social security numbers are masked, an adversary may be able to recognize an individual\u27s identity for a data record by looking at the values of quasi-identifiers (QID), known as identity disclosure, or can uncover sensitive attributes (SA) about an individual through attribute disclosure. In data privacy field, multiple disclosure risk measures have been proposed. These share two drawbacks: they do not consider identity and attribute disclosure concurrently, and they make restrictive assumptions on an adversary\u27s knowledge and disclosure target by assuming certain attributes are QIDs and SAs with clear boundary in between. In this study, we present a Flexible Adversary Disclosure Risk (FADR) measure that addresses these limitations, by presenting a single combined metric of identity and attribute disclosure, and considering all scenarios for an adversary’s knowledge and disclosure targets while providing the flexibility to model a specific disclosure preference. In addition, we employ FADR measure to develop our novel “RU Generalization” algorithm that anonymizes a sensitive dataset to be able to publish the data for public access while preserving the privacy of individuals in the dataset. The challenge is to preserve privacy without incurring excessive information loss. Our RU Generalization algorithm is a greedy heuristic algorithm, which aims at minimizing the combination of both disclosure risk and information loss, to obtain an optimized anonymized dataset. We have conducted a set of experiments on a benchmark dataset from 1994 Census database, to evaluate both our FADR measure and RU Generalization algorithm. We have shown the robustness of our FADR measure and the effectiveness of our RU Generalization algorithm by comparing with the benchmark anonymization algorithm

Statistical Disclosure Control for Micro-Data Using the R Package sdcMicro

The demand for data from surveys, censuses or registers containing sensible information on people or enterprises has increased significantly over the last years. However, before data can be provided to the public or to researchers, confidentiality has to be respected for any data set possibly containing sensible information about individual units. Confidentiality can be achieved by applying statistical disclosure control (SDC) methods to the data in order to decrease the disclosure risk of data.The R package sdcMicro serves as an easy-to-handle, object-oriented S4 class implementation of SDC methods to evaluate and anonymize confidential micro-data sets. It includes all popular disclosure risk and perturbation methods. The package performs automated recalculation of frequency counts, individual and global risk measures, information loss and data utility statistics after each anonymization step. All methods are highly optimized in terms of computational costs to be able to work with large data sets. Reporting facilities that summarize the anonymization process can also be easily used by practitioners. We describe the package and demonstrate its functionality with a complex household survey test data set that has been distributed by the International Household Survey Network

Do Not Touch My Data: Exploring a Disclosure-Based Framework to Address Data Access

Companies have too much control over people’s information. In the data marketplace, companies package and sell individuals’ data, and these individuals have little to no bargaining power over the process. Companies may freely buy and sell people’s data in the private sector for targeted marketing and behavior manipulation. In the justice system, an unchecked data marketplace leaves black and brown communities vulnerable to serious data access issues caused by predictive sentencing, for example. Risk assessment algorithms in predictive sentencing rely on data on individuals and run all relevant data points to provide the likelihood that a defendant will recidivate low risk, medium risk, or high risk. These algorithms are flawed and deeply biased because they use factors that correlate with race and socioeconomic status. The law should recognize people’s property interests in their data. Recognizing individuals’ property interests in their data sets up a robust disclosure-based solution. The disclosure-based solution gives individuals substantial control over their data. The Note proposes a centralized platform—the Private Information Reporting System—for individuals to know where their data is used and restrict companies from selling it. This will result in more power for individuals and equity in the justice system