A semantic framework for noise addition with nominal data

Leveraging the potential of synthetic text for AI in mental healthcare

In today's world it seems fair to say that extensive digital data sharing is the price we pay for the technological advances we have seen achieved as a result of AI systems analysing large quantities of data in a relatively short time. Where such AI is used in the realm of mental health, this data sharing poses additional challenges not just due to the sensitive nature of the data itself but also the potential vulnerability of the data donors themselves should there be a cybersecurity data breach. To address the problem, the AI community proposes to use synthetic text preserving only the salient properties of the original. Such text has potential to fill gaps in the textual data availability (e.g., rare conditions or under-represented groups) while reducing exposure. Our perspective piece is aimed to demystify the process of generating synthetic text, explain its algorithmic and ethical challenges, especially for the mental health domain, as well as most promising ways of overcoming them. We aim to promote better understanding and as a result acceptability of synthetic text outside the research community.

The Rise of Cloud Computing: Data Protection, Privacy, and Open Research Challenges-A Systematic Literature Review (SLR)

Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data. Therefore, we need certain data protection techniques to protect the sensitive data that is outsourced over the cloud. In this paper, we conduct a systematic literature review (SLR) to illustrate all the data protection techniques that protect sensitive data outsourced over cloud storage. Therefore, the main objective of this research is to synthesize, classify, and identify important studies in the field of study. Accordingly, an evidence-based approach is used in this study. Preliminary results are based on answers to four research questions. Out of 493 research articles, 52 studies were selected. 52 papers use different data protection techniques, which can be divided into two main categories, namely noncryptographic techniques and cryptographic techniques. Noncryptographic techniques consist of data splitting, data anonymization, and steganographic techniques, whereas cryptographic techniques consist of encryption, searchable encryption, homomorphic encryption, and signcryption. In this work, we compare all of these techniques in terms of data protection accuracy, overhead, and operations on masked data. Finally, we discuss the future research challenges facing the implementation of these techniques.

Recent Developments in Privacy-Preserving Mining of Clinical Data

With the dramatic increases in both the capability to collect personal data and the capability to analyze large amounts of data, increasingly sophisticated and personal insights are being drawn. These insights are valuable for clinical applications but also open up possibilities for identification and abuse of personal information. In this paper, we survey recent research on classical methods of privacy-preserving data mining. Looking at dominant techniques and recent innovations to them, we examine the applicability of these methods to the privacy-preserving analysis of clinical data. We also discuss promising directions for future research in this area.

A privacy-preserving design for sharing demand-driven patient datasets over permissioned blockchains and P2P secure transfer

Sharing patient datasets curated by health institutions is critical for the advance of monitoring, surveillance and research. However, patient data is sensitive data and it can only be released under certain conditions and with previous explicit consent. Privacy preserving data sharing provides techniques to distribute datasets minimizing the risk of identification of patients. However, the sharing of datasets is typically done without considering the needs or requests of data consumers. Blockchain technologies provide an opportunity to gather those requests and share and assemble datasets using privacy-preserving methods as data and requirements on anonymity match. The architecture and design of such a solution is described, assuming an underlying permissioned blockchain network where providers such as healthcare institutions deal with consent, patient preferences and anonymity guarantees, playing a mediator role to a network of organizations.

Semantic Disclosure Control: semantics meets data privacy

Purpose

To overcome the limitations of purely statistical approaches to data protection, the purpose of this paper is to propose Semantic Disclosure Control (SeDC): an inherently semantic privacy protection paradigm that, by relying on state of the art semantic technologies, rethinks privacy and data protection in terms of the meaning of the data.

Design/methodology/approach

The need for data protection mechanisms able to manage data from a semantic perspective is discussed and the limitations of statistical approaches are highlighted. Then, SeDC is presented by detailing how it can be enforced to detect and protect sensitive data.

Findings

So far, data privacy has been tackled from a statistical perspective; that is, available solutions focus just on the distribution of the data values. This contrasts with the semantic way by which humans understand and manage (sensitive) data. As a result, current solutions present limitations both in preventing disclosure risks and in preserving the semantics (utility) of the protected data.

Practical implications

SeDC captures more general, realistic and intuitive notions of privacy and information disclosure than purely statistical methods. As a result, it is better suited to protect heterogenous and unstructured data, which are the most common in current data release scenarios. Moreover, SeDC preserves the semantics of the protected data better than statistical approaches, which is crucial when using protected data for research.

Social implications

Individuals are increasingly aware of the privacy threats that the uncontrolled collection and exploitation of their personal data may produce. In this respect, SeDC offers an intuitive notion of privacy protection that users can easily understand. It also naturally captures the (non-quantitative) privacy notions stated in current legislations on personal data protection.

Originality/value

On the contrary to statistical approaches to data protection, SeDC assesses disclosure risks and enforces data protection from a semantic perspective. As a result, it offers more general, intuitive, robust and utility-preserving protection of data, regardless their type and structure.