Related papers: Designing a Location Trace Anonymization Contest
Evidence on the effectiveness of Man-At-The-End (MATE) software protections, such as code obfuscation, has mainly come from limited empirical research. Recently, however, an automatable method was proposed to obtain statistical models of…
We consider Location-based Service (LBS) settings, where a LBS provider logs the requests sent by mobile device users over a period of time and later wants to publish/share these logs. Log sharing can be extremely valuable for advertising,…
The International Fingerprint Liveness Detection Competition is an international biennial competition open to academia and industry with the aim to assess and report advances in Fingerprint Presentation Attack Detection. The proposed…
Publishing physical activity data can facilitate reproducible health-care research in several areas such as population health management, behavioral health research, and management of chronic health problems. However, publishing such data…
Statistics about traffic flow and people's movement gathered from multiple geographical locations in a distributed manner are the driving force powering many applications, such as traffic prediction, demand prediction, and restaurant…
The Internet of Things (IoT) promises to improve user utility by tuning applications to user behavior, but revealing the characteristics of a user's behavior presents a significant privacy risk. Our previous work has established the…
Many large-scale information systems such as intelligent transportation systems, smart grids or smart buildings collect data about the activities of their users to optimize their operations. To encourage participation and adoption of these…
Properly benchmarking a system is a difficult and intricate task. Unfortunately, even a seemingly innocuous benchmarking mistake can compromise the guarantees provided by a given systems security defense and also put its reproducibility and…
Upcoming WiFi-based localization systems for indoor environments face a conflict of privacy interests: Server-side localization violates location privacy of the users, while localization on the user's device forces the localization provider…
Banks routinely use neural networks to make decisions. While these models offer higher accuracy, they are susceptible to adversarial attacks, a risk often overlooked in the context of event sequences, particularly sequences of financial…
We have investigated a new application of adversarial examples, namely location privacy protection against landmark recognition systems. We introduce mask-guided multimodal projected gradient descent (MM-PGD), in which adversarial examples…
Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to guess if an input sample was used to train the model. In this paper, we show that prior…
Attribute inference - the process of analyzing publicly available data in order to uncover hidden information - has become a major threat to privacy, given the recent technological leap in machine learning. One way to tackle this threat is…
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…
In this work, we aim to clarify and reconcile metrics for evaluating privacy protection in text through a systematic survey. Although text anonymization is essential for enabling NLP research and model development in domains with sensitive…
Eye-tracking technology can aid in understanding neurodevelopmental disorders and tracing a person's identity. However, this technology poses a significant risk to privacy, as it captures sensitive information about individuals and…
Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we…
Social graphs are widely used in research (e.g., epidemiology) and business (e.g., recommender systems). However, sharing these graphs poses privacy risks because they contain sensitive information about individuals. Graph anonymization…
Contact tracing is being widely employed to combat the spread of COVID-19. Many apps have been developed that allow for tracing to be done automatically based off location and interaction data generated by users. There are concerns,…
The success of applications for sharing GPS trajectories raises serious privacy concerns, in particular about users' home addresses. In this paper we show that a Bayesian approach is natural and effective for a rigorous analysis of…