Large Language Model (LLM) inference services like ChatGPT are popular for enabling diverse tasks via prompts, yet they exacerbate privacy risks due to the potential exposure of sensitive data in user inputs. Existing local differential privacy (LDP)-based text sanitization mechanisms offer lightweight protection suitable for cloud-based LLM inference. Nevertheless, uniform privacy budget allocation and generalized sanitization mechanisms neglect the critical protection needs of sensitive user data, such as Personally Identifiable Information (PII). Empirical evidence of this work reveals that even with a strict privacy budget (ϵ=0.1), the sensitive information leakage rate can reach an alarmingly high 71.74%. To address these challenges, this paper proposes Rap-LI, a risk-aware privacy preservation framework for LLM inference, designed to be plug-and-play. Rap-LI performs risk identification and personalized labeling on user prompts, then develops a risk-aware LDP mechanism for text sanitization, formally proven to satisfy both token-level and sentence-level LDP guarantees. Extensive experimental results demonstrate Rap-LI’s superior privacy-utility balance. It improves privacy protection against sensitive information leakage by an average of 51.68% compared to methods with comparable utility. Our code is available at https://github.com/Cristliu/RapLI.

Cloud environments enhance diffusion model efficiency but introduce privacy risks, including intellectual property theft and data breaches. As AI-generated images gain recognition as copyright-protected works, ensuring their security and intellectual property protection in cloud environments has become a pressing challenge. This paper addresses privacy protection in diffusion model inference under cloud environments, identifying two key characteristics—denoising-encryption antagonism and stepwise generative nature—that create challenges such as incompatibility with traditional encryption, incomplete input parameter representation, and inseparability of the generative process. We propose PPIDM (Privacy-PreservingInference forDiffusionModels), a framework that balances efficiency and privacy by retaining lightweight text encoding and image decoding on the client while offloading computationally intensive U-Net layers to multiple non-colluding cloud servers. Client-side aggregation reduces computational overhead and enhances security. Experiments show PPIDM offloads 67% of Stable Diffusion computations to the cloud, reduces image leakage by 75%, and maintains high output quality (PSNR = 36.9, FID = 4.56), comparable to standard outputs. PPIDM offers a secure and efficient solution for cloud-based diffusion model inference.

In this article, we propose a novel hybrid control approach for solving the multirobot motion planning (MRMP) problem in the Internet of Things (IoT) environment, where the closed workspace is “abstracted” as a discrete-event system (DES) that is modeled as a timed Petri net. In the DES, robot’s movement between adjacent confliction zones is viewed as a “transition,” by taking the robot kinematics into consideration. To ensure safety and efficiency, robots should not appear at the same conflict position at the same time and should not block each other. To this end, an online IoT-based approach is provided to control robots to travel in the closed workspace safely (without collisions) and efficiently (without deadlocks). Specifically, a high-level supervisory controller whose commands are in the form of transitions, is used to control robots to travel in the closed workspace. At the low level, we explicitly capture the time-driven robot kinematics, and commands from the supervisory controller are translated into appropriate input signals to the actuators of the robots, which in turn influence states of the high-level DES. Simulation results demonstrate the expressiveness of the proposed model and the effectiveness and efficiency of the proposed algorithm.

Frequent data breaches in the cloud environment have seriously affected cloud subscribers and providers. Privacy‐preserving image retrieval methods can improve the security of cloud image retrieval; however, existing methods have limited accuracy on dynamically updated image databases and mobile lightweight devices. In this study, we propose a privacy‐preserving image retrieval method based on disordered local histograms and vision transformer in cloud computing, by designing a multiple encryption method and transformer‐based feature model to better mine the local feature value of encrypted images. Specifically, the user performs different value substitution, position substitution, and color substitution on the subblocks of the image to protect the image information. The cloud server extracts the unordered local histogram from the encrypted image and generates retrievable features using transformer. Experiments show that compared with similar CNN schemes, the retrieval accuracy of this method is improved by 8.5%, and the retrieval efficiency is improved by 54.8%.

With the rapid development of interactive multimedia services and camera sensor networks, the number of network videos is exploding, which has formed a natural carrier library for steganography. In this study, a coverless steganography scheme based on motion analysis of video is proposed. For every video in the database, the robust histograms of oriented optical flow (RHOOF) are obtained, and the index database is constructed. The hidden information bits are mapped to the hash sequences of RHOOF, and the corresponding indexes are sent by the sender. At the receiver, through calculating hash sequences of RHOOF from the cover video, the secret information can be extracted successfully. During the whole process, the cover video remains original without any modification and has a strong ability to resist steganalysis. The capacity is investigated and shows good improvement. The robustness performance is prominent against most attacks such as pepper and salt noise, speckle noise, MPEG-4 compression, and motion JPEG 2000 compression. Compared with the existing coverless information hiding schemes based on images, the proposed method not only obtains a good trade-off between hiding information capacity and robustness but also can achieve higher hiding success rate and lower transmission data load, which shows good practicability and feasibility.