Related papers: Privacy-preserving Cross-domain Routing Optimizati…
MapReduce is a popular programming model and an associated implementation for parallel processing big data in the distributed environment. Since large scaled MapReduce data centers usually provide services to many users, it is an essential…
The Domain Name System (DNS) was created to resolve the IP addresses of the web servers to easily remembered names. When it was initially created, security was not a major concern; nowadays, this lack of inherent security and trust has…
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…
Distributed energy resources (DERs) are gaining prominence due to their advantages in improving energy efficiency, reducing carbon emissions, and enhancing grid resilience. Despite the increasing deployment, the potential of DERs has yet to…
The OpenFlow-based SDN is widely studied to better network performance through planning fine-grained paths. However, being designed to configure path hop-by-hop, it faces the scalability issue that both the flow table overhead and path…
Wi-Fi signals may help realize low-cost and non-invasive human sensing, yet it can also be exploited by eavesdroppers to capture private information. Very few studies rise to handle this privacy concern so far; they either jam all sensing…
Decentralized optimization is crucial for multi-agent systems, with significant concerns about communication efficiency and privacy. This paper explores the role of efficient communication in decentralized stochastic gradient descent…
This study investigates the optimal selection of parameters for collaborative clustering while ensuring data privacy. We focus on key clustering algorithms within a collaborative framework, where multiple data owners combine their data. A…
The absence of security and privacy measures between DNS recursive resolvers and authoritative nameservers has been exploited by both on-path and off-path attackers. Although numerous security proposals have been introduced in practice and…
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…
Differential privacy (DP) is widely employed to provide privacy protection for individuals by limiting information leakage from the aggregated data. Two well-known models of DP are the central model and the local model. The former requires…
gRPC is at the heart of modern distributed system architectures. Based on HTTP/2 and Protocol Buffers, it provides highly performant, standardized, and polyglot communication across loosely coupled microservices and is increasingly…
This paper tackles the challenging and practical problem of multi-identifier private user profile matching for privacy-preserving ad measurement, a cornerstone of modern advertising analytics. We introduce a comprehensive cryptographic…
In the rapidly evolving landscape of wireless networks, achieving enhanced throughput with low latency for data transmission is crucial for future communication systems. While low complexity OSPF-type solutions have shown effectiveness in…
Privacy issues and communication cost are both major concerns in distributed optimization. There is often a trade-off between them because the encryption methods required for privacy-preservation often incur expensive communication…
Although distributed Gaussian process regression (GPR) enables multiple agents with separate datasets to jointly learn a model of the target function, its collaborative nature poses risks of private data leakage. To address this, we propose…
The foreseen growing role of outsourced machine learning services is raising concerns about the privacy of user data. Several technical solutions are being proposed to address the issue. Hardware security modules in cloud data centres…
In this work, we propose a novel framework for privacy-preserving client-distributed machine learning. It is motivated by the desire to achieve differential privacy guarantees in the local model of privacy in a way that satisfies all…
Clustering is a fundamental data processing task used for grouping records based on one or more features. In the vertically partitioned setting, data is distributed among entities, with each holding only a subset of those features. A key…
The advance of quantum computing poses a significant threat to classical cryptography, compromising the security of current encryption schemes such as RSA and ECC. In response to this challenge, two main approaches have emerged: quantum…