Related papers: Shai: Enforcing Data-Specific Policies with Near-Z…
Protecting sensitive information in data-driven collaborations, such as AI training, while meeting the diverse requirements of multiple mutually distrusted stakeholders, is both crucial and challenging. This paper presents Styx, a novel…
Many smartphone apps transmit personally identifiable information (PII), often without the users knowledge. To address this issue, we present PrivacyProxy, a system that monitors outbound network traffic and generates app-specific…
The critical need for transparent and trustworthy machine learning in cybersecurity operations drives the development of this integrated Explainable AI (XAI) framework. Our methodology addresses three fundamental challenges in deploying AI…
This study investigates the adoption and effectiveness of AI-based anomaly detection in cross-provider electronic health record (EHR) environments. It aims to (1) identify the organisational and digital capabilities required for successful…
Data cleaning is a time-consuming process that depends on the data analysis that users perform. Existing solutions treat data cleaning as a separate offline process that takes place before analysis begins. Applying data cleaning before…
Selective data protection is a promising technique to defend against the data leakage attack. In this paper, we revisit technical challenges that were neglected when applying this protection to real applications. These challenges include…
With the advent of numerous online content providers, utilities and applications, each with their own specific version of privacy policies and its associated overhead, it is becoming increasingly difficult for concerned users to manage and…
Ensuring the proper use of sensitive data in analytics under complex privacy policies is an increasingly critical challenge. Many existing approaches lack portability, verifiability, and scalability across diverse data processing…
Self-Sovereign Identity (SSI) is a new distributed method for identity management, commonly used to address the problem that users are lack of control over their identities. However, the excessive pursuit of self-sovereignty in the most…
Outsourcing data into the cloud becomes popular thanks to the pay-as-you-go paradigm. However, such practice raises privacy concerns. The conventional way to achieve data privacy is to encrypt sensitive data before outsourcing. When data…
Exact similarity search over large collections of data series is a fundamental operation in modern applications, yet existing solutions are often fragmented, specialized, or tailored to specific execution environments. In this paper, we…
Privacy and security have rapidly emerged as first order design constraints. Users now demand more protection over who can see their data (confidentiality) as well as how it is used (control). Here, existing cryptographic techniques for…
Data sharing among partners---users, organizations, companies---is crucial for the advancement of data analytics in many domains. Sharing through secure computation and differential privacy allows these partners to perform private…
Chai empowers users to create and interact with customized chatbots, offering unique and engaging experiences. Despite the exciting prospects, the work recognizes the inherent challenges of a commitment to modern safety standards.…
This paper focuses on similarity caching systems, in which a user request for an {object~$o$} that is not in the cache can be (partially) satisfied by a similar stored {object~$o'$}, at the cost of a loss of user utility. Similarity caching…
Social networks have become an essential meeting point for millions of individuals willing to publish and consume huge quantities of heterogeneous information. Some studies have shown that the data published in these platforms may contain…
In the modern digital world, a user of a smart system remains surrounded with as well as observed by a number of tiny IoT devices round the clock almost everywhere. Unfortunately, the ability of these devices to sense and share various…
Pervasiveness of tracking devices and enhanced availability of spatially located data has deepened interest in using them for various policy interventions, through computational data analysis tasks such as spatial hot spot detection. In…
Private Set Intersection (PSI) is a vital cryptographic technique used for securely computing common data of different sets. In PSI protocols, often two parties hope to find their common set elements without needing to disclose their…
LLM-powered search services have driven data integration as a significant trend. However, this trend's progress is fundamentally hindered, despite the fact that combining individual knowledge can significantly improve the relevance and…