The widespread adoption of Transformer models raises critical privacy concerns as users must expose sensitive inputs to service providers during inference. While existing secure Transformer frameworks have addressed this issue to some extent, most rely on interactive protocols with prohibitive communication overhead, limiting practicality in bandwidth-constrained scenarios. This paper introduces Zephyr, a secure and non-interactive two-party inference framework for Transformers that overcomes these limitations. First, Zephyr proposes two novel SIMD ciphertext decompression techniques, shifting from serial to parallel processing to accelerate decompression by 1.2× while preserving accuracy. Second, Zephyr optimizes the deployment strategy of bootstrapping operations (which refresh encrypted data noise) during computation. This allows using smaller encryption parameters while achieving 1.1× faster bootstrapping than NEXUS. Evaluated on BERT-base under challenging 100Mbps/80ms conditions, Zephyr demonstrates superior performance - 24.3× faster than Iron(NeurIPS22), 2.1× faster than BOLT, and 11% faster than NEXUS while reducing communication costs by 95% versus BOLT(Oakland24) and 32% versus NEXUS(NDSS25), making it particularly effective for bandwidth-constrained environments while maintaining security against semi-honest adversaries.

As the Internet of Things (IoT) is booming, the transmission speed of data in the network is getting more and more attention. Network coding is an effective technique to improve network throughput. In network coding, the encoded packets must be integrity-checked to prevent pollution attacks. Some linearly homomorphic signature (LHS) schemes based on bilinear pairs have been used to check the integrity of packets, and so far the scheme LZL20 is the most efficient signature scheme among them. Here, we first analyze the security model and signature structure of LZL20, and find that there is a security vulnerability in the scheme. Experiments show that for a 12–18 kB file, our signature forgery algorithm can forge a message/signature pair with 100% probability within 3–5 ms. Then, we construct a LHS scheme with higher signature efficiency and shorter signature length. In random oracle model, we proved the scheme is existentially unforgeable under adaptive chosen message attacks. We theoretically analyze our signature length to be 320 bits shorter than LZL20. Finally, we implement our scheme, and for a 12–18 kB file, experiments show that the signature time of our scheme is 60.93%–62.59% of that of LZL20.

Today’s apps on a mobile device, such as a smartphone and a tablet, need to access various resources to deliver quality service to users’ satisfaction. These resources may include cameras, microphones, screens, processors, various specialized sensors, and data. In today’s client-server framework, resources accessible to an app are limited to those available in the device running the app, on the cloud, and likely in a few statically connected devices. However, there can be abundant resources on devices near the app-running one with desirable functionalities that can enable or empower the app’s new features and services, but cannot be easily accessed and leveraged. The NEOP (Neutron Operation Platform) framework is an app development and execution environment that removes the barrier across the devices. Heterogeneous IoT devices make the capabilities in their hardware and service software available after security and privacy authentication. An app is developed as a composition of capabilities distributed across various end devices and the cloud. Its constituent computing tasks can be dynamically created and scheduled. Different device capabilities can be selectively and dynamically recruited into the app for the optimal user experience. In this paper we describe example scenarios that motivate the next-generation app framework, the framework’s architecture, design principles, technical challenges, and details on its design and implementation. We also compare this work with related efforts on distributed mobile computing to highlight the unique contributions made by the NEOP platform.

Side-channel attacks on cryptographic chips in embedded systems have been attracting considerable interest from the field of information security in recent years. Many research studies have contributed to improve the side-channel attack efficiency, in which most of the works assume the noise of the encryption signal has a linear stable Gaussian distribution. However, their performances of noise reduction were moderate. Thus, in this paper, we describe a highly effective data-preprocessing technique for noise reduction based on empirical mode decomposition (EMD) and demonstrate its application for a side-channel attack. EMD is a time-frequency analysis method for nonlinear unstable signal processing, which requires no prior knowledge about the cryptographic chip. During the procedure of data preprocessing, the collected traces will be self-adaptably decomposed into sum of several intrinsic mode functions (IMF) based on their own characteristics. And then, meaningful IMF will be reorganized to reduce its noise and increase the efficiency of key recovering through correlation power analysis attack. This technique decreases the total number of traces for key recovering by 17.7%, compared to traditional attack methods, which is verified by attack efficiency analysis of the SM4 block cipher algorithm on the FPGA power consumption analysis platform.

The Internet of Things (IoT) provides the network for physical devices, like home appliances, embedded with electronics, sensors, and software, to share and exchange data. With its fast development, security of IoT has become a crucial problem. Among the methods of attack, side‐channel attack has proven to be an effective tool to compromise the security of different devices with improving techniques of data processing, like DPA and CPA. Meanwhile, many countermeasures have risen accordingly as well, such as masking and noise addition. However, their common deficiency was that every single countermeasure might not be able to protect the key information completely after statistical analysis. Sensitive information will be disclosed during differential power analysis of Sbox, since it is the only nonlinear component in block cipher. Thus, how to protect Sbox effectively was the highlight of researches. Based on Sbox‐reuse concept proposed by Bilgin, this paper put forward a new type of a countermeasure scheme against DPA in multi‐Sbox of block cipher. We first converted the multi‐Sbox into 4 × 4 permutations and then reused permutation with the algebraic degree of more than one so as to turn it into a special reusable Sbox and then numbered 4 × 4 permutation input. Finally, we made these inputs of permutations completely random by masking. Since it was necessary to make the collected power consumption curve subject to alignment process in DPA by chosen‐plaintext attack, this scheme combined the concept from DPA countermeasures of masking and noise addition. After the experiment with the proposed implementation, successful prevention of the attacker from accurately aligning the power consumption curve of the target Sbox has been proven, and the level of security has been improved by adding more random noise to protect key information and decrease the accuracy of statistical analysis.