An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices

Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S. statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy

Ontology-Based Quality Evaluation of Value Generalization Hierarchies for Data Anonymization

In privacy-preserving data publishing, approaches using Value Generalization Hierarchies (VGHs) form an important class of anonymization algorithms. VGHs play a key role in the utility of published datasets as they dictate how the anonymization of the data occurs. For categorical attributes, it is imperative to preserve the semantics of the original data in order to achieve a higher utility. Despite this, semantics have not being formally considered in the specification of VGHs. Moreover, there are no methods that allow the users to assess the quality of their VGH. In this paper, we propose a measurement scheme, based on ontologies, to quantitatively evaluate the quality of VGHs, in terms of semantic consistency and taxonomic organization, with the aim of producing higher-quality anonymizations. We demonstrate, through a case study, how our evaluation scheme can be used to compare the quality of multiple VGHs and can help to identify faulty VGHs.Comment: 18 pages, 7 figures, presented in the Privacy in Statistical Databases Conference 2014 (Ibiza, Spain

Emerging privacy challenges and approaches in CAV systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions

Generating Artificial Data for Private Deep Learning

In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy with respect to the original dataset. We use generative adversarial network to draw privacy-preserving artificial data samples and derive an empirical method to assess the risk of information disclosure in a differential-privacy-like way. Our experiments show that we are able to generate artificial data of high quality and successfully train and validate machine learning models on this data while limiting potential privacy loss.Comment: Privacy-Enhancing Artificial Intelligence and Language Technologies, AAAI Spring Symposium Series, 201

Privacy Preservation by Disassociation

In this work, we focus on protection against identity disclosure in the publication of sparse multidimensional data. Existing multidimensional anonymization techniquesa) protect the privacy of users either by altering the set of quasi-identifiers of the original data (e.g., by generalization or suppression) or by adding noise (e.g., using differential privacy) and/or (b) assume a clear distinction between sensitive and non-sensitive information and sever the possible linkage. In many real world applications the above techniques are not applicable. For instance, consider web search query logs. Suppressing or generalizing anonymization methods would remove the most valuable information in the dataset: the original query terms. Additionally, web search query logs contain millions of query terms which cannot be categorized as sensitive or non-sensitive since a term may be sensitive for a user and non-sensitive for another. Motivated by this observation, we propose an anonymization technique termed disassociation that preserves the original terms but hides the fact that two or more different terms appear in the same record. We protect the users' privacy by disassociating record terms that participate in identifying combinations. This way the adversary cannot associate with high probability a record with a rare combination of terms. To the best of our knowledge, our proposal is the first to employ such a technique to provide protection against identity disclosure. We propose an anonymization algorithm based on our approach and evaluate its performance on real and synthetic datasets, comparing it against other state-of-the-art methods based on generalization and differential privacy.Comment: VLDB201