Related papers: PEARL: Plausibly Deniable Flash Translation Layer …
Sensitive information is present on our phones, disks, watches and computers. Its protection is essential. Plausible deniability of stored data allows individuals to deny that their device contains a piece of sensitive information. This…
Mobile computing devices have been used broadly to store, manage and process sensitive or even mission critical data. To protect confidentiality of data stored in mobile devices, major mobile operating systems use full disk encryption,…
Data privacy is critical in instilling trust and empowering the societal pacts of modern technology-driven democracies. Unfortunately, it is under continuous attack by overreaching or outright oppressive governments, including some of the…
Nowadays, mobile devices have been used broadly to store and process sensitive data. To ensure confidentiality of the sensitive data, Full Disk Encryption (FDE) is often integrated in mainstream mobile operating systems like Android and…
Local Differential Privacy (LDP) is a widely adopted privacy-protection model in the Internet of Things (IoT) due to its lightweight, decentralized, and scalable nature. However, it is vulnerable to poisoning attacks, and existing defenses…
Speculative decoding (SD), where an extra draft model is employed to provide multiple draft tokens first, and then the original target model verifies these tokens in parallel, has shown great power for LLM inference acceleration. However,…
We present PEARL (Peer-Enhanced Adaptive Radio via On-Device LLM), a framework for cooperative cross-layer optimization in device-to-device (D2D) communication. Building on our previous work on single-device on-device LLMs, PEARL extends…
Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capabilities of Large Language Models (LLMs) and is now being applied to Vision-Language Models (VLMs). However, vanilla RLVR for VLMs verifies…
In many deployed systems, new text inputs are handled by retrieving similar past cases, for example when routing and responding to citizen messages in digital governance platforms. When these systems fail, the problem is often not the…
Deep hiding, embedding images into another using deep neural networks, has shown its great power in increasing the message capacity and robustness. In this paper, we conduct an in-depth study of state-of-the-art deep hiding schemes and…
In light of the significant progress made in the development and application of semantic segmentation tasks, there has been increasing attention towards improving the robustness of segmentation models against natural degradation factors…
Large Language Models show great potential with external tools, but face significant challenges in complex, multi-turn tool invocation. They often exhibit weak planning, tool hallucination, erroneous parameter generation, and struggle with…
Physical layer security (PLS) is a promising technology to secure wireless communications by exploiting the physical properties of the wireless channel. However, the passive nature of PLS creates a significant imbalance between the effort…
While Large Language Models (LLMs) achieve remarkable performance through training on massive datasets, they can exhibit concerning behaviors such as verbatim reproduction of training data rather than true generalization. This memorization…
Encryption is a vital tool of information technology protecting our data in the world with ubiquitous computers. While photons are regarded as ideal information carriers, it is a must to implement such data protection on all-optical…
Prices of NAND flash memories are falling drastically due to market growth and fabrication process mastering while research efforts from a technological point of view in terms of endurance and density are very active. NAND flash memories…
A novel method, the Pareto Envelope Augmented with Reinforcement Learning (PEARL), has been developed to address the challenges posed by multi-objective problems, particularly in the field of engineering where the evaluation of candidate…
Physical layer deception (PLD) is a novel security mechanism that combines physical layer security (PLS) with deception technologies to actively defend against eavesdroppers. In this paper, we establish a novel semantic model for PLD that…
The goal of physical layer security (PLS) is to make use of the properties of the physical layer, including the wireless communication medium and the transceiver hardware, to enable critical aspects of secure communications. In particular,…
Text-to-image diffusion models have demonstrated remarkable effectiveness in rapid and high-fidelity personalization, even when provided with only a few user images. However, the effectiveness of personalization techniques has lead to…