On Utilizing Association and Interaction Concepts for Enhancing Microaggregation in Secure Statistical Databases

This paper presents a possibly pioneering endeavor to tackle the microaggregation techniques (MATs) in secure statistical databases by resorting to the principles of associative neural networks (NNs). The prior art has improved the available solutions to the MAT by incorporating proximity information, and this approach is done by recursively reducing the size of the data set by excluding points that are farthest from the centroid and points that are closest to these farthest points. Thus, although the method is extremely effective, arguably, it uses only the proximity information while ignoring the mutual interaction between the records. In this paper, we argue that interrecord relationships can be quantified in terms of the following two entities: 1) their ldquoassociationrdquo and 2) their ldquointeraction.rdquo This case means that records that are not necessarily close to each other may still be ldquogrouped,rdquo because their mutual interaction, which is quantified by invoking transitive-closure-like operations on the latter entity, could be significant, as suggested by the theoretically sound principles of NNs. By repeatedly invoking the interrecord associations and interactions, the records are grouped into sizes of cardinality ldquok,rdquo where k is the security parameter in the algorithm. Our experimental results, which are done on artificial data and benchmark real-life data sets, demonstrate that the newly proposed method is superior to the state of the art not only based on the information loss (IL) perspective but also when it concerns a criterion that involves a combination of the IL and the disclosure risk (DR)

On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases

We consider the micro-aggregation problem which involves partitioning a set of individual records in a micro-data file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the micro-data file, is known to be NP-hard, and has been tackled using many heuristic solutions. In this paper, we would like to demonstrate that in the process of developing micro-aggregation techniques (MATs), it is expedient to incorporate information about the dependence between the random variables in the micro-data file. This can be achieved by pre-processing the micro-data before invoking any MAT, in order to extract the useful dependence information from the joint probability distribution of the variables in the micro-data file, and then accomplishing the micro-aggregation on the "maximally independent" variables-thus confirming the conjecture [A conjecture, which was recently proposed by Domingo-Ferrer et al. (IEEE Trans Knowl Data Eng 14(1):189-201, 2002), was that the phenomenon of micro-aggregation can be enhanced by incorporating dependence-based information between the random variables of the micro-data file by working with (i.e., selecting) the maximally independent variables. Domingo-Ferrer et al. have proposed to select one variable from among the set of highly correlated variables inferred via the correlation matrix of the micro-data file. In this paper, we demonstrate that this process can be automated, and that it is advantageous to select the "most independent variables" by using methods distinct from those involving the correlation matrix.] of Domingo-Ferrer et al. Our results, on real life and artificial data sets, show that including such information will enhance the process of determining how many variables are to be used, and which of them should be used in the micro-aggregation process

Semantic Partitioning and Machine Learning in Sentiment Analysis

This paper investigates sentiment analysis in Arabic tweets that have the presence of Jordanian dialect. A new dataset was collected during the coronavirus disease (COVID-19) pandemic. We demonstrate two models: the Traditional Arabic Language (TAL) model and the Semantic Partitioning Arabic Language (SPAL) model to envisage the polarity of the collected tweets by invoking several, well-known classifiers. The extraction and allocation of numerous Arabic features, such as lexical features, writing style features, grammatical features, and emotional features, have been used to analyze and classify the collected tweets semantically. The partitioning concept was performed on the original dataset by utilizing the hidden semantic meaning between tweets in the SPAL model before invoking various classifiers. The experimentation reveals that the overall performance of the SPAL model competes over and better than the performance of the TAL model due to imposing the genuine idea of semantic partitioning on the collected dataset

A survey on statistical disclosure control and micro-aggregation techniques for secure statistical databases

This paper surveys the fields of Statistical Disclosure Control (SDC) and Micro-Aggregation Techniques (MATs), which are both areas fundamental to the science of secure Statistical DataBases (SDBs). The paper is written from the perspective of a computer scientist with the hope that it will prove to be a source of reference material useful to researchers and practitioners in the field. The paper first introduces the concept of SDC and describes the domain of its applications and the various data types that are currently used in SDBs. It then proceeds to focus on the family of micro-data types in SDBs. At this juncture, we introduce the importance of the relevant measures, namely the metrics termed as the Information Loss (IL) and the Disclosure Risk (DR), after which we survey the various methods of resolving the conflicting goals that these metrics represent. Thereafter, the paper summarizes the perturbative and non-perturbative SDC methods for micro-data protection, and it focuses on the families of MATs by formally stating the Micro-Aggregation Problem and surveying it in a comprehensive manner. Apart from the paper including a historical view of the field of MATs, it describes a broad selection of work that has been reported more recently. Indeed, we believe that this paper represents a complete overview of the state-of-the-art techniques. Copyrigh

Enhancing micro-aggregation technique by utilizing dependence-based information in secure statistical databases

We consider the Micro-Aggregation Problem (MAP) in secure statistical databases which involves partitioning a set of individual records in a micro-data file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the micro-data file, is known to be NP-hard, and has been tackled using many heuristic solutions. In this paper, we would like to demonstrate that in the process of developing Micro-Aggregation Techniques (MATs), it is expedient to incorporate information about the dependence between the random variables in the micro-data file. This can be achieved by pre-processing the micro-data before invoking any MAT, in order to extract the useful dependence information from the joint probability distribution of the variables in the micro-data file, and then accomplishing the micro-aggregation on the "maximally independent" variables. Our results, on real life data sets, show that including such information will enhance the process of determining how many variables are to be used, and which of them should be used in the micro-aggregation process

An AI-based causal strategy for securing statistical databases using micro-aggregation

Although Artificial Intelligent (AI) techniques have been used in various applications, their use in maintaining security in Statistical DataBases (SDBs) has not been reported. This paper presents results, that to the best of our knowledge is pioneering, by which concepts from causal networks are used to secure SDBs. We consider the Micro-Aggregation Problem (MAP) in secure SDBs which involves partitioning a set of individual records in a micro-data file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the micro-data file, is known to be NP-hard, and has been tackled using many heuristic solutions. In this paper, we would like to demonstrate that in the process of developing Micro-Aggregation Techniques (MATs), it is expedient to incorporate AI-based causal information about the dependence between the random variables in the micro-data file. This can be achieved by pre-processing the micro-data before invoking any MAT, in order to extract the useful dependence information from the joint probability distribution of the variables in the micro-data file, and then accomplishing the micro-aggregation on the "maximally independent" variables. Our results, on artificial life data sets, show that including such information will enhance the process of determining how many variables are to be used, and which of them should be used in the micro-aggregation process

On utilizing association and interaction concepts for enhancing microaggregation in secure statistical databases

This paper presents a possibly pioneering endeavor to tackle the Microaggregation Techniques (MATs) in secure statistical databases by resorting to the principles of associative neural networks (NNs). The prior art has improved the available solutions to the MAT by incorporating proximity information, and this approach is done by recursively reducing the size of the data set by excluding points that are farthest from the centroid and points that are closest to these farthest points. Thus, although the method is extremely effective, arguably, it uses only the proximity information while ignoring the mutual interaction between the records. In this paper, we argue that interrecord relationships can be quantified in terms of the following two entities: 1) their "association" and 2) their "interaction." This case means that records that are not necessarily close to each other may still be "grouped," because their mutual interaction, which is quantified by invoking transitive-closure-like operations on the latter entity, could be significant, as suggested by the theoretically sound principles of NNs. By repeatedly invoking the interrecord associations and interactions, the records are grouped into sizes of cardinality "κ," where κ is the security parameter in the algorithm. Our experimental results, which are done on artificial data and benchmark real-life data sets, demonstrate that the newly proposed method is superior to the state of the art not only based on the Information Loss (IL) perspective but also when it concerns a criterion that involves a combination of the IL and the Disclosure Risk (DR)

Achieving microaggregation for secure statistical databases using fixed-structure partitioning-based learning automata

We consider the microaggregation problem (MAP) that involves partitioning a set of individual records in a microdata file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the microdata file, is known to be NP-hard and has been tackled using many heuristic solutions. In this paper, we present the first reported fixed-structure-stochastic-automata -based solution to this problem. The newly proposed method leads to a lower value of the information loss (IL), obtains a better tradeoff between the IL and the disclosure risk (DR) when compared with state-of-the-art methods, and leads to a superior value of the scoring index, which is a criterion involving a combination of the IL and the DR. The scheme has been implemented, tested, and evaluated for different real-life and simulated data sets. The results clearly demonstrate the applicability of learning automata to the MAP and its ability to yield a solution that obtains the best tradeoff between IL and DR when compared with the state of the art

On optimizing the k-Ward micro-aggregation technique for secure statistical databases

We consider the problem of securing a statistical database by utilizing the well-known micro-aggregation strategy, and in particular, the k-Ward strategy introduced in [1] and utilized in [2]. The latter scheme, which represents the state-of-the-art, coalesces the sorted data attribute values into groups, and on being queried, reports the means of the corresponding groups. We demonstrate that such a scheme can be optimized on two fronts. First of all, we minimize the computations done in evaluating the between-class distance matrix, to require only a constant number of updating distance computations. Secondly, and more importantly, we propose that the data set be partitioned recursively before a k-Ward strategy is invoked, and that the latter be invoked on the "primitive" sub-groups which terminate the recursion. Our experimental results, done on two benchmark data sets, demonstrate a marked improvement. While the information loss is comparable to the k-Ward micro-aggregation technique proposed by Domingo-Ferrer1 et. al. [2], the computations required to achieve this loss is a fraction of the computations required in the latter - providing a computational advantage which sometimes exceeds 80% if one method is used by itself, and more than 90% if both enhancements are invoked simultaneously