相关论文: Practical Attacks Against Privacy and Availability…

Consumer Internet of Things (IoT) devices are increasingly common, from smart speakers to security cameras, in homes. Along with their benefits come potential privacy and security threats. To limit these threats a number of commercial…

In collaborative learning, clients keep their data private and communicate only the computed gradients of the deep neural network being trained on their local data. Several recent attacks show that one can still extract private information…

Large Language Models (LLMs) are increasingly deployed in sensitive domains including healthcare, legal services, and confidential communications, where privacy is paramount. This paper introduces Whisper Leak, a side-channel attack that…

With the fast development of Information Technology, a tremendous amount of data have been generated and collected for research and analysis purposes. As an increasing number of users are growing concerned about their personal information,…

When users leave their mobile devices unattended, or let others use them momentarily, they are susceptible to privacy breaches. Existing technological defenses, such as unlock authentication or account switching, have proven to be…

Large Language Models increasingly power critical infrastructure from healthcare to finance, yet their vulnerability to adversarial manipulation threatens system integrity and user safety. Despite growing deployment, no comprehensive…

Large Language Models (LLMs) have shown greatly enhanced performance in recent years, attributed to increased size and extensive training data. This advancement has led to widespread interest and adoption across industries and the public.…

Transfer learning is widely used for transferring knowledge from a source domain to the target domain where the labeled data is scarce. Recently, deep transfer learning has achieved remarkable progress in various applications. However, the…

In a brave new age of global connectivity and e-commerce, interconnections via networks have heightened, creating for both individuals and organizations, a state of complete dependence upon vulnerable systems for storage and transfer of…

Local Differential Privacy (LDP) offers strong privacy protection, especially in settings in which the server collecting the data is untrusted. However, designing LDP mechanisms that achieve an optimal trade-off between privacy, utility and…

All mobile devices are energy-constrained. They use batteries that allows using the device for a limited amount of time. In general, energy attacks on mobile devices are denial of service (DoS) type of attacks. While previous studies have…

In this letter, we propose three schemes designed to detect attacks over the air interface in cellular networks. These decision rules rely on the generalized likelihood ratio test, and are fed by data that can be acquired using common…

Data at the physical layer transmits via media such as copper cable, fiber optic, or wireless. Physical attack vectors exist that challenge data confidentiality and availability. Protocols and encryption standards help obfuscate but often…

In this work we present definitive evidence, analysis, and (where needed) speculation to answer the questions, (1) Which concrete security measures in mobile devices meaningfully prevent unauthorized access to user data? (2) In what ways…

Transient execution attacks, also called speculative execution attacks, have drawn much interest as they exploit the transient execution of instructions, e.g., during branch prediction, to leak data. Transient execution is fundamental to…

Large Language Model (LLM) systems are inherently compositional, with individual LLM serving as the core foundation with additional layers of objects such as plugins, sandbox, and so on. Along with the great potential, there are also…

Today, we use smartphones as multi-purpose devices that communicate with their environment to implement context-aware services, including asset tracking, indoor localization, contact tracing, or access control. As a de-facto standard,…

The inference process of modern large language models (LLMs) demands prohibitive computational resources, rendering them infeasible for deployment on consumer-grade devices. To address this limitation, recent studies propose distributed LLM…

The leakage of data might have been an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection system are prone to leakage.…

Protecting personal information privacy has become a controversial issue among online social network providers and users. Most social network providers have developed several techniques to decrease threats and risks to the users privacy.…