相关论文: Improving Privacy-Preserving Techniques for Smart …

Distributed stochastic optimization enables multi-agent collaboration in applications such as distributed learning and sensor networks, but also raises critical privacy concerns due to the involvement of sensitive data. While existing…

The stochastic block model (SBM) and degree-corrected block model (DCBM) are network models often selected as the fundamental setting in which to analyze the theoretical properties of community detection methods. We consider the problem of…

Off-grid networks are recently emerging as a solution to connect the unconnected or provide alternative services to networks of possibly untrusted participants. The systems currently used, however, exhibit limitations due to their…

Despite Federated Learning (FL) employing gradient aggregation at the server for distributed training to prevent the privacy leakage of raw data, private information can still be divulged through the analysis of uploaded gradients from…

Secure Multi-Party Computation (MPC) is an area of cryptography that enables computation on sensitive data from multiple sources while maintaining privacy guarantees. However, theoretical MPC protocols often do not scale efficiently to…

Industrial Internet of Things (IIoT) systems have become integral to smart manufacturing, yet their growing connectivity has also exposed them to significant cybersecurity threats. Traditional intrusion detection systems (IDS) often rely on…

Privacy Preserving Data Mining is a method which ensures privacy of individual information during mining. Most important task involves retrieving information from multiple data bases which is distributed. The data once in the data warehouse…

This paper studies a $K$-user lattice-code based multiple-access (LCMA) scheme. Each user equipment (UE) encode its message with a practical lattice code, where we suggest a $2^m$-ary \emph{ring code} with symbol-wise bijective mapping to…

In traditional runtime verification, a system is typically observed by a monolithic monitor. Enforcing privacy in such settings is computationally expensive, as it necessitates heavy cryptographic primitives. Therefore, privacy-preserving…

Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, and resource allocation. Existing decentralized optimization algorithms require sharing explicit state information among the…

Data on the Web has fueled much of the recent progress in AI. As more high-quality data becomes difficult to access, synthetic data is emerging as a promising solution for privacy-friendly data release and complementing real datasets in…

A new nonlinear Rao-Nam like symmetric key encryption scheme is presented in this paper. QC-LDPC lattices that are practically implementable in high dimensions due to their low complexity encoding and decoding algorithms, are used in our…

Density-adaptive domain discretization is essential for high-utility privacy-preserving analytics but remains challenging under Local Differential Privacy (LDP) due to the privacy-budget costs associated with iterative refinement. We…

As one of the largest and most complex systems on earth, power grid (PG) operation and control have stepped forward as a compound analysis on both physical and cyber layers which makes it vulnerable to assaults from economic and security…

In the wake of the burgeoning expansion of generative artificial intelligence (AI) services, the computational demands inherent to these technologies frequently necessitate cloud-powered computational offloading, particularly for…

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…

Infrastructure maintenance is inherently complex, especially for widely dispersed transport systems like roads and railroads. Maintaining this infrastructure involves multiple partners working together to ensure safe, efficient upkeep that…

Graph clustering under the framework of differential privacy, which aims to process graph-structured data while protecting individual privacy, has been receiving increasing attention. Despite significant achievements in current research,…

Data marketplaces (DMs) promote the benefits of the Internet of Things (IoT) in smart cities. To facilitate the easy exchanges of real-time IoT data streams between device owners and third-party applications, it is required to provide…

Secure linear aggregation is to linearly aggregate private inputs of different users with privacy protection. The server in a federated learning (FL) environment can fulfill any linear computation on private inputs of users through the…