This paper contributes an in-depth investigation (N=24) of privacy perceptions in the context of medical data donation apps. Medical data donation refers to the act of voluntarily sharing medical data with research institutions, which plays a crucial role in advancing healthcare research and personalized medicine. To design effective medical data donation apps, we need to understand how privacy expectations affect people's willingness to use such apps. We focus on non-users—those who have no experience with medical data donation apps—because gaining a deeper understanding of their perceptions is essential for fostering the adoption of these apps. Our findings highlight the importance of trust, transparency, and anonymity as driving factors. Participants expressed a willingness to share highly sensitive medical data with the apps if they were assured of complete anonymity, yet criticism regarding the risks of de-anonymization was also raised. Based on our results, we identify privacy awareness issues, especially concerning data sensitivity. Additionally, we explain the differences between participants' privacy expectations and preferences and what existing medical data donation apps offer. Finally, we provide guidance for the development of future user-centric medical data donation apps.

With the increased performance of large language models (LLMs), conversational agents (CA), such as ChatGPT, are nowadays available to any individual requiring little technical knowledge and skills. Initial studies that have investigated related privacy risks primarily focused on either technical aspects and misuse of these tools, or captured overall perceptions of CA users in small-scale qualitative evaluations. Complementing and extending previous work, we used a quantitative user-centered approach to analyze and compare the behaviors and concerns of users and non-users. We conducted a survey study (N=422) with (1) service users, i.e., users of CA services, (2) local users, i.e., users of a local instance of CA (partially local users, or fully local users), and (3) non-users. We collected self-reported usage patterns and personal data-sharing behavior as well as privacy concerns related to different types of personal data (e.g., health data, demographics, or opinions). Furthermore, we analyze individuals' intention to use CA services in multiple scenarios. Our findings show that users of CA services generally have fewer privacy concerns than non-users. While users rarely share data related to personal identifiers and account credentials, they tend to often share data related to lifestyle, health, standard of living, and opinions. Surprisingly, partially local users tend to share more data with CA services as they also generally use CA services more often and for more diverse purposes. Also, while the majority of CA services users declared not being willing to prioritize CA services as an information source in the described scenarios such as seeking legal advice, between about one-quarter and one-third of partially local users would use CA services for all scenarios. Furthermore, half of the users were willing to stop using CA for privacy reasons (e.g., in case of data leaks), whereas a large majority of non-users reported not using CAs simply because they do not have the need or the opportunity. Our work highlights the high privacy risks for CA services users as CA services largely expand the amount of any type of personal information that can be collected by companies.

Current anonymous communication systems either provide strong anonymity with significant delay or low latency with unreliable anonymity. This division leads to smaller user bases and reduced anonymity as users choose systems based on their specific requirements. To address this issue, we propose an approach based on mix networks that employs two mixing techniques on mix nodes. Each technique offers distinct anonymity and latency guarantees—one for users valuing strong anonymity and another for those with specific latency constraints. We conducted an in-depth empirical study to evaluate the effectiveness of our proposal. The evaluation results demonstrate that our approach provides much more protection than the traditional method of using just one mixing technique on mix nodes. It offers enhanced anonymity for all users without impacting any user’s latency requirements. Furthermore, our findings indicate that our proposal eliminates the need for generating cover traffic to improve anonymity, achieving this improvement without introducing the bandwidth overhead associated with cover traffic.

Medical data donation involves voluntarily sharing medical data with research institutions, which is crucial for advancing healthcare research. However, the sensitive nature of medical data poses privacy and security challenges. The primary concern is the risk of de-anonymization, where users can be linked to their donated data through background knowledge or communication metadata. In this paper, we introduce Anonify, a decentralized anonymity protocol offering strong user protection during data donation without reliance on a single entity. It achieves dual-level anonymity protection, covering both communication and data aspects by leveraging Distributed Point Functions, and incorporating k-anonymity and stratified sampling within a secret-sharing-based setting. Anonify ensures that the donated data is in a form that affords flexibility for researchers in their analyses. Our evaluation demonstrates the efficiency of Anonify in preserving privacy and optimizing data utility. Furthermore, the performance of machine learning algorithms on the anonymized datasets generated by the protocol shows high accuracy and precision.

Anonymous microblogging systems are known to be vulnerable to intersection attacks due to network churn. An adversary that monitors all communications can leverage the churn to learn who is publishing what with increasing confidence over time. In this paper, we propose a protocol for mitigating intersection attacks in anonymous microblogging systems by grouping users into anonymity sets based on similarities in their publishing behavior. The protocol provides a configurable communication schedule for users in each set to manage the inevitable trade-off between latency and bandwidth overhead. In our evaluation, we use real-world datasets from two popular microblogging platforms, Twitter and Reddit, to simulate user publishing behavior. The results demonstrate that the protocol can protect users against intersection attacks at low bandwidth overhead when the users adhere to communication schedules. In addition, the protocol can sustain a slow degradation in the size of the anonymity set over time under various churn rates.

Publish/Subscribe systems like Twitter and Reddit let users communicate with many recipients without requiring prior personal connections. The content that participants of these systems publish and subscribe to is typically public, but they may nevertheless wish to remain anonymous. While many existing systems allow users to omit explicit identifiers, they do not address the obvious privacy risks of being associated with content that may contain a wide range of sensitive information. We present 2PPS (Twice-Private Publish-Subscribe), the first pub/sub protocol to deliver strong provable privacy protection for both publishers and subscribers, leveraging Distributed Point Function-based secret sharing for publishing and Private Information Retrieval for subscribing. 2PPS does not require trust in other clients and its privacy guarantees hold as long as even a single honest server participant remains. Furthermore, it is scalable and delivers latency suitable for microblogging applications. A prototype implementation of 2PPS can handle 100,000 concurrent active clients with 5 seconds end-to-end latency and significantly lower bandwidth requirements than comparable systems.