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There is much discussion and debate about how to improve the security and privacy of mobile communication systems, both voice and data. Most proposals attempt to provide incremental improvements to systems that are deployed today. Indeed,…
By inducing privacy attacks on NLP models, attackers can obtain sensitive information such as training data and model parameters, etc. Although researchers have studied, in-depth, several kinds of attacks in NLP models, they are…
Today, the publication of microdata poses a privacy threat. Vast research has striven to define the privacy condition that microdata should satisfy before it is released, and devise algorithms to anonymize the data so as to achieve this…
Network data needs to be shared for distributed security analysis. Anonymization of network data for sharing sets up a fundamental tradeoff between privacy protection versus security analysis capability. This privacy/analysis tradeoff has…
Hypothesis testing is a statistical inference framework for determining the true distribution among a set of possible distributions for a given dataset. Privacy restrictions may require the curator of the data or the respondents themselves…
The use of synthetic data in health applications raises privacy concerns, yet the lack of open frameworks for privacy evaluations has slowed its adoption. A major challenge is the absence of accessible benchmark datasets for evaluating…
Privacy by Design (PbD) is the most common approach followed by software developers who aim to reduce risks within their application designs, yet it remains commonplace for developers to retain little conceptual understanding of what is…
A plethora of contact tracing apps have been developed and deployed in several countries around the world in the battle against Covid-19. However, people are rightfully concerned about the security and privacy risks of such applications. To…
The scientific method presents a key challenge to privacy because it requires many samples to support a claim. When samples are commercially valuable or privacy-sensitive enough, their owners have strong reasons to avoid releasing them for…
This paper focuses on the design and analysis of privacy-preserving techniques for group testing and infection status retrieval. Our work is motivated by the need to provide accurate information on the status of disease spread among a group…
In pseudonymous online fora like Reddit, the benefits of self-disclosure are often apparent to users (e.g., I can vent about my in-laws to understanding strangers), but the privacy risks are more abstract (e.g., will my partner be able to…
Despite increasing uptake, there are still many concerns as to the security of virtual assistant hubs (such as Google Nest and Amazon Alexa) in the home. Consumer fears have been somewhat exacerbated by widely-publicised privacy breaches,…
One of the main challenges faced by a user today is protecting their privacy, especially during widespread surveil- lance. This led to the development of privacy infrastructures whose main purpose is to guarantee users privacy. However,…
Fine-tuning has emerged as a critical process in leveraging Large Language Models (LLMs) for specific downstream tasks, enabling these models to achieve state-of-the-art performance across various domains. However, the fine-tuning process…
Privacy against an adversary (AD) that tries to detect the underlying privacy-sensitive data distribution is studied. The original data sequence is assumed to come from one of the two known distributions, and the privacy leakage is measured…
The ability to analyze network threats is very important in security research. Traditional approaches, involving sandboxing technology are limited to simulating a single host, missing local network attacks. This issue is addressed by…
Context: As mobile applications (Apps) widely spread over our society and life, various personal information is constantly demanded by Apps in exchange for more intelligent and customized functionality. An increasing number of users are…
Artificial intelligence systems introduce complex privacy risks throughout their lifecycle, especially when processing sensitive or high-dimensional data. Beyond the seven traditional privacy threat categories defined by the LINDDUN…
A major impediment to research on improving peer review is the unavailability of peer-review data, since any release of such data must grapple with the sensitivity of the peer review data in terms of protecting identities of reviewers from…
The public sharing of user information opens the door for adversaries to infer private data, leading to privacy breaches and facilitating malicious activities. While numerous studies have concentrated on privacy leakage via public user…