Related papers: P-MOD: Secure Privilege-Based Multilevel Organizat…
An increasing number of businesses are replacing their data storage and computation infrastructure with cloud services. Likewise, there is an increased emphasis on performing analytics based on multiple datasets obtained from different data…
Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data…
Privacy-preserving machine learning (PPML) based on cryptographic protocols has emerged as a promising paradigm to protect user data privacy in cloud-based machine learning services. While it achieves formal privacy protection, PPML often…
The growing complexity of Internet of Things (IoT) environments, particularly in cross-domain data sharing, presents significant security challenges. Existing data-sharing schemes often rely on computationally expensive cryptographic…
Cloud Computing (CC) is revolutionizing the way IT resources are delivered to users, allowing them to access and manage their systems with increased cost-effectiveness and simplified infrastructure. However, with the growth of CC comes a…
The digital economy is powered by a continuous and massive exchange of personal data. Individuals provide data to platforms in return for services, from social networking and search to health monitoring, entertainment, and access to LLMs.…
Securing cloud configurations is an elusive task, which is left up to system administrators who have to base their decisions on ``trial and error'' experimentations or by observing good practices (e.g., CIS Benchmarks). We propose a…
Outsourcing a relational database to the cloud offers several benefits, including scalability, availability, and cost-effectiveness. However, there are concerns about the security and confidentiality of the outsourced data. A general…
Gradient inversion attacks pose significant privacy threats to distributed training frameworks such as federated learning, enabling malicious parties to reconstruct sensitive local training data from gradient communications between clients…
Imagine a group of citizens willing to collectively contribute their personal data for the common good to produce socially useful information, resulting from data analytics or machine learning computations. Sharing raw personal data with a…
High performance computing clusters operating in shared and batch mode pose challenges for processing sensitive data. In the meantime, the need for secure processing of sensitive data on HPC system is growing. In this work we present a…
Organizations use data lakes to store and analyze sensitive data. But hackers may compromise data lake storage to bypass access controls and access sensitive data. To address this, we propose Membrane, a system that (1) cryptographically…
With the development of sensor network, mobile computing, and web applications, data are now collected from many distributed sources to form big datasets. Such datasets can be hosted in the cloud to achieve economical processing. However,…
We consider a distributed multi-user secret sharing (DMUSS) setting in which there is a dealer, $n$ storage nodes, and $m$ secrets. Each user demands a $t$-subset of $m$ secrets. Earlier work in this setting dealt with the case of $t=1$; in…
Federated learning is an improved version of distributed machine learning that further offloads operations which would usually be performed by a central server. The server becomes more like an assistant coordinating clients to work together…
Many techniques for privacy-preserving data mining (PPDM) have been investigated over the past decade. Often, the entities involved in the data mining process are end-users or organizations with limited computing and storage resources. As a…
There are a lot of on going efforts in the research community as well as industry around providing privacy-preserving and secure storage for personal data. Although, over time it has adopted many tag lines such as Personal Information Hub…
As the volume of stored data continues to grow, identifying and protecting sensitive information within large repositories becomes increasingly challenging, especially when shared with multiple users with different roles and permissions.…
Cloud computing has been shown to be an essential enabling technology for public sector organizations PSOs and offers numerous potential benefits, including reduced information technology infrastructure costs, increased innovation…
In the current paradigm of digital personalized services, the centralized management of personal data raises significant privacy concerns, security vulnerabilities, and diminished individual autonomy over sensitive information. Despite…