Related papers: Anonymity-washing
The development of artificial intelligence has significantly transformed people's lives. However, it has also posed a significant threat to privacy and security, with numerous instances of personal information being exposed online and…
Over the recent years, the availability of datasets containing personal, but anonymized information has been continuously increasing. Extensive research has revealed that such datasets are vulnerable to privacy breaches: being able to…
The protection of personal data has become a central topic in software development, especially with the implementation of the General Data Protection Law (LGPD) in Brazil and the General Data Protection Regulation (GDPR) in the European…
Text anonymization is the process of removing or obfuscating information from textual data to protect the privacy of individuals. This process inherently involves a complex trade-off between privacy protection and information preservation,…
This paper focuses on some shortcomings in current privacy and data protection regulations' ability to adequately address the ramifications of AI-driven data processing practices, in particular where data sets are combined and processed by…
Sanitizing sensitive text data typically involves removing personally identifiable information (PII) or generating synthetic data under the assumption that these methods adequately protect privacy; however, their effectiveness is often only…
The increased use of text data in social science research has benefited from easy-to-access data (e.g., Twitter). That trend comes at the cost of research requiring sensitive but hard-to-share data (e.g., interview data, police reports,…
In the recent time, the problem of protecting privacy in statistical data before they are published has become a pressing one. Many reliable studies have been accomplished, and loads of solutions have been proposed. Though, all these…
There are currently two approaches to anonymization: "utility first" (use an anonymization method with suitable utility features, then empirically evaluate the disclosure risk and, if necessary, reduce the risk by possibly sacrificing some…
The principle of data minimization aims to reduce the amount of data collected, processed or retained to minimize the potential for misuse, unauthorized access, or data breaches. Rooted in privacy-by-design principles, data minimization has…
Data protection algorithms are becoming increasingly important to support modern business needs for facilitating data sharing and data monetization. Anonymization is an important step before data sharing. Several organizations leverage on…
With large volumes of detailed health care data being collected, there is a high demand for the release of this data for research purposes. Hospitals and organizations are faced with conflicting interests of releasing this data and…
We consider the privacy problem in data publishing: given a relation I containing sensitive information 'anonymize' it to obtain a view V such that, on one hand attackers cannot learn any sensitive information from V, and on the other hand…
Data privacy is one of the key challenges faced by enterprises today. Anonymization techniques address this problem by sanitizing sensitive data such that individual privacy is preserved while allowing enterprises to maintain and share…
As mobile app usage continues to rise, so does the generation of extensive user interaction data, which includes actions such as swiping, zooming, or the time spent on a screen. Apps often collect a large amount of this data and claim to…
Background knowledge is an important factor in privacy preserving data publishing. Distribution-based background knowledge is one of the well studied background knowledge. However, to the best of our knowledge, there is no existing work…
Numerous generalization techniques have been proposed for privacy preserving data publishing. Most existing techniques, however, implicitly assume that the adversary knows little about the anonymization algorithm adopted by the data…
As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data. A significant subset of these approaches incorporate differentially private…
The Internet of Things' potential for major privacy invasion is a concern. This paper reports on a systematic literature review of privacy-preserving solutions appearing in the research literature and in the media. We analysed proposed…
Software logs, generated during the runtime of software systems, are essential for various development and analysis activities, such as anomaly detection and failure diagnosis. However, the presence of sensitive information in these logs…