Related papers: Would you mind being watched by machines? Privacy …
Privacy attacks on machine learning models aim to identify the data that is used to train such models. Such attacks, traditionally, are studied on static models that are trained once and are accessible by the adversary. Motivated to meet…
The upcoming Beyond 5G (B5G) and 6G networks are expected to provide enhanced capabilities such as ultra-high data rates, dense connectivity, and high scalability. It opens many possibilities for a new generation of services driven by…
The discourse on privacy risks in Large Language Models (LLMs) has disproportionately focused on verbatim memorization of training data, while a constellation of more immediate and scalable privacy threats remain underexplored. This…
These days, deep learning models have achieved great success in multiple fields, from autonomous driving to medical diagnosis. These models have expanded the abilities of artificial intelligence by offering great solutions to complex…
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…
We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach,…
In the current data driven era, synthetic data, artificially generated data that resembles the characteristics of real world data without containing actual personal information, is gaining prominence. This is due to its potential to…
Data protection is the process of securing sensitive information from being corrupted, compromised, or lost. A hyperconnected network, on the other hand, is a computer networking trend in which communication occurs over a network. However,…
Of growing concern in privacy scholarship is artificial intelligence (AI), as a powerful producer of inferences. Taken to its limits, AI may be presumed capable of inferring "everything from everything," thereby making untenable any…
Users care greatly about preserving the privacy of their personal data gathered during their use of information systems. This extends to both the data they actively provide in exchange for services as well as the metadata passively…
Privacy technologies have become extremely prevalent in recent years from secure communication channels to the Tor network. These technologies were designed to provide privacy and security for users, but these ideals have also led to…
Human activity detection has seen a tremendous growth in the last decade playing a major role in the field of pervasive computing. This emerging popularity can be attributed to its myriad of real-life applications primarily dealing with…
Machine learning models leak information about the datasets on which they are trained. An adversary can build an algorithm to trace the individual members of a model's training dataset. As a fundamental inference attack, he aims to…
Since the beginning of the Internet thirty years ago, we have witnessed a number of changes in the application of communication technologies. Today, the Internet can be described to a large extent as a ubiquitous infrastructure that is…
When building machine learning models using sensitive data, organizations should ensure that the data processed in such systems is adequately protected. For projects involving machine learning on personal data, Article 35 of the GDPR…
Protecting personal information privacy has become a controversial issue among online social network providers and users. Most social network providers have developed several techniques to decrease threats and risks to the users privacy.…
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…
Given the many technical, social, and policy shifts in access to scholarly content since the early days of text data mining, it is time to expand the conversation about text data mining from concerns of the researcher wishing to mine data…
Artificial intelligence and data access are already mainstream. One of the main challenges when designing an artificial intelligence or disclosing content from a database is preserving the privacy of individuals who participate in the…
We investigate the contents of web-scraped data for training AI systems, at sizes where human dataset curators and compilers no longer manually annotate every sample. Building off of prior privacy concerns in machine learning models, we…