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Secure multiparty computation (MPC) allows joint privacy-preserving computations on data of multiple parties. Although MPC has been studied substantially, building solutions that are practical in terms of computation and communication cost…
Everyday, large amounts of sensitive data is distributed across mobile phones, wearable devices, and other sensors. Traditionally, these enormous datasets have been processed on a single system, with complex models being trained to make…
Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets. In real-world settings, this approach is hindered by expensive communication and privacy concerns. Both of these…
Scientific collaborations benefit from collaborative learning of distributed sources, but remain difficult to achieve when data are sensitive. In recent years, privacy preserving techniques have been widely studied to analyze distributed…
Cloud applications need network data encryption to isolate from other tenants and protect their data from potential eavesdroppers in the network infrastructure. This paper presents SMT, a protocol design for emerging datacenter transport…
With explosively escalating service demands, beyond fifth generation (B5G) aims to realize various requirements for multi-service networks, i.e., higher performance of mixed enhanced mobile broadband (eMBB) and ultra-reliable low-latency…
We introduce CTCP, a reliable transport protocol using network coding. CTCP is designed to incorporate TCP features such as congestion control, reliability, and fairness while significantly improving on TCP's performance in lossy,…
-Multipath communications at the Internet scale have been a myth for a long time, with no actual protocol being deployed so that multiple paths could be taken by a same connection on the way towards an Internet destination. Recently, the…
Mobile applications continuously generate DNS queries that can reveal sensitive user behavioral patterns even when communications are encrypted. This paper presents a privacy enhancement framework based on query forgery to protect users…
Network slicing has emerged as a key network technology, providing network operators with the means to offer virtual networks to vertical users over a single physical network infrastructure. Recent research has resulted mainly in techniques…
Network slicing in 3GPP 5G system architecture has introduced significant improvements in the flexibility and efficiency of mobile communication. However, this new functionality poses challenges in maintaining the privacy of mobile users,…
Class-of-service (CoS) network traffic classification (NTC) classifies a group of similar traffic applications. The CoS classification is advantageous in resource scheduling for Internet service providers and avoids the necessity of…
Achieving communication efficiency in decentralized machine learning has been attracting significant attention, with communication compression recognized as an effective technique in algorithm design. This paper takes a first step to…
Website Fingerprinting (WFP) uses deep learning models to classify encrypted network traffic to infer visited websites. While historically effective, prior methods fail to generalize to modern web environments. Single-page applications…
Distributed computing frameworks such as MapReduce have become essential for large-scale data processing by decomposing tasks across multiple nodes. The multi-access distributed computing (MADC) model further advances this paradigm by…
The proliferation of smartphone devices has led to the emergence of powerful user services from enabling interactions with friends and business associates to mapping, finding nearby businesses and alerting users in real-time. Moreover,…
Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack…
Over the years, use of smartphones has come to dominate several areas, improving our lives, offering us convenience, and reshaping our daily work circumstances. Beyond traditional use for communication, they are used for many peripheral…
The era of Big Data has brought with it a richer understanding of user behavior through massive data sets, which can help organizations optimize the quality of their services. In the context of transportation research, mobility data can…
Desktops and laptops can be maliciously exploited to violate privacy. In this paper, we consider the daily battle between the passive attacker who is targeting a specific user against a user that may be adversarial opponent. In this…