Identifying information from data using an organizational goals ontology: a case of the Australian economy / Tengku Adil Tengku Izhar and Torab Torabi.

Organizational data is important to assist domain experts and entrepreneurs for decision making process in relation to the organizational goals. The trustworthiness of organizational data in relation to achieving the organizational goals is often questioned because of the vast amount of organizational data. This paper proposes a methodology to evaluate organizational data that relates to the organizational goals. This refers to the importance of assisting the organization to utilize relevance of organizational data from the vast amount of datasets for decision-making in relation to the organizational goals. The aim of this paper is to evaluate the level of the organizational goals achievement. In order to achieve this aim, we identify the dependency relationship between organizational data and organizational goals. Based on this dependency relationship, we define a metrics to analyse organizational data to be considered relevant to the organizational goals achievement. The case study is present to test the applicability of the methodology to measure the level of the Australian economy. The results show the dependency relationship between the case study goal and its variables. The contribution of this paper will serve as a new approach in evaluating the level of the organizational goals achievement

Supervised learning using a symmetric bilinear form for record linkage

Record Linkage is used to link records of two different files corresponding to the same individuals. These algorithms are used for database integration. In data privacy, these algorithms are used to evaluate the disclosure risk of a protected data set by linking records that belong to the same individual. The degree of success when linking the original (unprotected data) with the protected data gives an estimation of the disclosure risk. In this paper we propose a new parameterized aggregation operator and a supervised learning method for disclosure risk assessment. The parameterized operator is a symmetric bilinear form and the supervised learning method is formalized as an optimization problem. The target of the optimization problem is to find the values of the aggregation parameters that maximize the number of re-identification (or correct links). We evaluate and compare our proposal with other non-parametrized variations of record linkage, such as those using the Mahalanobis distance and the Euclidean distance (one of the most used approaches for this purpose). Additionally, we also compare it with other previously presented parameterized aggregation operators for record linkage such as the weighted mean and the Choquet integral. From these comparisons we show how the proposed aggregation operator is able to overcome or at least achieve similar results than the other parameterized operators. We also study which are the necessary optimization problem conditions to consider the described aggregation functions as metric functions

Data privacy

Data privacy studies methods, tools, and theory to avoid the disclosure of sensitive information. Its origin is in statistics with the goal to ensure the confidentiality of data gathered from census and questionnaires. The topic was latter introduced in computer science and more particularly in data mining, where due to the large amount of data currently available, has attracted the interest of researchers, practitioners, and companies. In this paper we will review the main topics related to data privacy and privacy-enhancing technologies

Spherical microaggregation : anonymizing sparse vector spaces

Unstructured texts are a very popular data type and still widely unexplored in the privacy preserving data mining field. We consider the problem of providing public information about a set of confidential documents. To that end we have developed a method to protect a Vector Space Model (VSM), to make it public even if the documents it represents are private. This method is inspired by microaggregation, a popular protection method from statistical disclosure control, and adapted to work with sparse and high dimensional data sets

Automatic privacy and utility evaluation of anonymized documents via deep learning

Text anonymization methods are evaluated by comparing their outputs with human-based anonymizations through standard information retrieval (IR) metrics. On the one hand, the residual disclosure risk is quantified with the recall metric, which gives the proportion of re-identifying terms successfully detected by the anonymization algorithm. On the other hand, the preserved utility is measured with the precision metric, which accounts the proportion of masked terms that were also annotated by the human experts. Nevertheless, because these evaluation metrics were meant for information retrieval rather than privacy-oriented tasks, they suffer from several drawbacks. First, they assume a unique ground truth, and this does not hold for text anonymization, where several masking choices could be equally valid to prevent re-identification. Second, annotation-based evaluation relies on human judgements, which are inherently subjective and may be prone to errors. Finally, both metrics weight terms uniformly, thereby ignoring the fact that the influence on the disclosure risk or on utility preservation of some terms may be much larger than of others. To overcome these drawbacks, in this thesis we propose two novel methods to evaluate both the disclosure risk and the utility preserved in anonymized texts. Our approach leverages deep learning methods to perform this evaluation automatically, thereby not requiring human annotations. For assessing disclosure risks, we propose using a re-identification attack, which we define as a multi-class classification task built on top of state-of-the art language models. To make it feasible, the attack has been designed to capture the means and computational resources expected to be available at the attacker's end. For utility assessment, we propose a method that measures the information loss incurred during the anonymization process, which relies on a neural masked language modeling. We illustrate the effectiveness of our methods by evaluating the disclosure risk and retained utility of several well-known techniques and tools for text anonymization on a common dataset. Empirical results show significant privacy risks for all of them (including manual anonymization) and consistently proportional utility preservation

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