Incremental k-Anonymous microaggregation in large-scale electronic surveys with optimized scheduling

Improvements in technology have led to enormous volumes of detailed personal information made available for any number of statistical studies. This has stimulated the need for anonymization techniques striving to attain a difficult compromise between the usefulness of the data and the protection of our privacy. k-Anonymous microaggregation permits releasing a dataset where each person remains indistinguishable from other k–1 individuals, through the aggregation of demographic attributes, otherwise a potential culprit for respondent reidentification. Although privacy guarantees are by no means absolute, the elegant simplicity of the k-anonymity criterion and the excellent preservation of information utility of microaggregation algorithms has turned them into widely popular approaches whenever data utility is critical. Unfortunately, high-utility algorithms on large datasets inherently require extensive computation. This work addresses the need of running k-anonymous microaggregation efficiently with mild distortion loss, exploiting the fact that the data may arrive over an extended period of time. Specifically, we propose to split the original dataset into two portions that will be processed subsequently, allowing the first process to start before the entire dataset is received, while leveraging the superlinearity of the microaggregation algorithms involved. A detailed mathematical formulation enables us to calculate the optimal time for the fastest anonymization, as well as for minimum distortion under a given deadline. Two incremental microaggregation algorithms are devised, for which extensive experimentation is reported. The theoretical methodology presented should prove invaluable in numerous data-collection applications, including largescale electronic surveys in which computation is possible as the data comes in.Peer ReviewedPostprint (published version

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

Efficient k-anonymous microaggregation of multivariate numerical data via principal component analysis

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/k-Anonymous microaggregation is a widespread technique to address the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers, in applications where preserving the utility of the information disclosed is critical. Unfortunately, microaggregation methods with high data utility may impose stringent computational demands when dealing with datasets containing a large number of records and attributes. This work proposes and analyzes various anonymization methods which draw upon the algebraic-statistical technique of principal component analysis (PCA), in order to effectively reduce the number of attributes processed, that is, the dimension of the multivariate microaggregation problem at hand. By preserving to a high degree the energy of the numerical dataset and carefully choosing the number of dominant components to process, we manage to achieve remarkable reductions in running time and memory usage with negligible impact in information utility. Our methods are readily applicable to high-utility SDC of large-scale datasets with numerical demographic attributes.Peer ReviewedPostprint (author's final draft

Incremental k-Anonymous microaggregation in large-scale electronic surveys with optimized scheduling

Improvements in technology have led to enormous volumes of detailed personal information made available for any number of statistical studies. This has stimulated the need for anonymization techniques striving to attain a difficult compromise between the usefulness of the data and the protection of our privacy. k-Anonymous microaggregation permits releasing a dataset where each person remains indistinguishable from other k–1 individuals, through the aggregation of demographic attributes, otherwise a potential culprit for respondent reidentification. Although privacy guarantees are by no means absolute, the elegant simplicity of the k-anonymity criterion and the excellent preservation of information utility of microaggregation algorithms has turned them into widely popular approaches whenever data utility is critical. Unfortunately, high-utility algorithms on large datasets inherently require extensive computation. This work addresses the need of running k-anonymous microaggregation efficiently with mild distortion loss, exploiting the fact that the data may arrive over an extended period of time. Specifically, we propose to split the original dataset into two portions that will be processed subsequently, allowing the first process to start before the entire dataset is received, while leveraging the superlinearity of the microaggregation algorithms involved. A detailed mathematical formulation enables us to calculate the optimal time for the fastest anonymization, as well as for minimum distortion under a given deadline. Two incremental microaggregation algorithms are devised, for which extensive experimentation is reported. The theoretical methodology presented should prove invaluable in numerous data-collection applications, including largescale electronic surveys in which computation is possible as the data comes in.Peer Reviewe

Sustainable Smart Cities and Smart Villages Research

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Union’s Cohesion Policy and Common Agricultural Policy Arguably, the concept of ‘the village’ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of ‘smart village’. It highlights in which ways ‘smart village’ is distinct from ‘smart city’. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe