Due to high convenience and efficiency, electronic auction technology has been developed rapidly and has been applied to many online trading market applications. As more attention has been paid to information security, the privacy issues in the electronic auction have been widely studied. Differential privacy, as a lightweight privacy protection method, is an important direction in privacy preserving auction mechanism designing. However, most of the existing researches on differential privacy-based auction mechanism have not proposed a theoretical privacy inference attack method against the auction market. Therefore, the existence of privacy attacks is questionable, and the necessity and privacy protection performance of the existing differential privacy auction mechanism cannot be verified. To this end, in this paper we addressed the privacy attack issue and privacy protection issue in the auction market simultaneously. First, a Bayesian-based inference attack method against the double auction market was proposed from the perspective of the adversary. Theoretical analysis and evaluation results showed that the proposed inference attack method can effectively infer the bidding information of the target bidders, and attack success rate achieved approximately 95%. Second, an individual differential privacy-based auction mechanism was proposed from the perspective of the auction platform. Since not all the bidders will be attacked, we introduced the concept of individual differential privacy to provide targeted defense for specific bidders. Theoretical analysis demonstrated that the proposed auction mechanism satisfies 2 $\varepsilon$ -individual differential privacy. And the extensive evaluation results showed that, compared with the existing differential privacy-based auction mechanism, our proposed mechanism provided the best privacy protection performance, that is, reduced the attack success rate to 20%, and ensured better auction performance, such as social welfare and satisfaction ratio, than the other mechanisms. Note to Practitioners—In this paper, we addressed the non-invasive privacy issues in the widely used electronic auction mechanism. Most of the previous works focused on designing differential privacy based auction mechanism against the non-invasive privacy attack, but neglecting the principle of non-invasive privacy attack methods. This makes it impossible to verify the privacy protection effectiveness of their proposed mechanisms. For this reason, a very large privacy budget may be selected to ensure the efficiency of privacy protection, which will lead to poor auction performance. To this end, we first proposed a Bayesian-based inference attack method against the double auction market. The proposed inference attack method allows the adversary infer the bidders’ private bidding information by the public auction results. Moreover, we then proposed an individual differential privacy auction mechanism, which aimed to achieve effective privacy protection while minimizing the added noise, thereby improving auction performance. The experiments demonstrate that the proposed Bayesian-based inference attack method achieves a good attack successful rate, and the proposed individual differential privacy auction mechanism will achieve the better efficiency of privacy protection as well as auction performance comparing with the exist differential privacy-based auction mechanism. In conclusion, this paper provides a verification method for the future research on the privacy protection of electronic auction mechanism. Meanwhile, this paper proposes an efficient privacy protection auction mechanism, which can be used in various trading scenarios.

The IPv6 security architecture, IPSec, plays a positive role in the protection of IPv6 networks. To some special attacks, especially DDoS attacks, IPSec appears relatively weak, because IPSec can only defend against DDoS attacks that spoof their source addresses. In cases where attackers launch DDoS attacks with their real identity, IPSec is helpless. This paper proposes a deterministic link signature based DDoS IP traceback algorithm. It can immediately reconstruct the entire attack path after suffering a DDoS attack whether or not the source addresses are spoofed. To verify the validity of our algorithm, we implemented it under a simulated IPv6 environment with the OMNeT++ IPv6Suite.

Perception of digitized depth is a prerequisite for enabling the intelligence of three-dimensional (3D) laparoscopic systems. In this context, stereo matching of laparoscopic stereoscopic images presents a promising solution. However, the current research in this field still faces challenges. First, the acquisition of accurate depth labels in a laparoscopic environment proves to be a difficult task. Second, errors in the correction of laparoscopic images are prevalent. Finally, laparoscopic image registration suffers from ill-posed regions such as specular highlights and textureless areas. In this paper, we make significant contributions by developing (1) a correction compensation module to overcome correction errors; (2) an adaptive cost aggregation module to improve prediction performance in ill-posed regions; (3) a novel self-supervised stereo matching framework based on these two modules. Specifically, our framework rectifies features and images based on learned pixel offsets, and performs differentiated aggregation on cost volumes based on their value. The experimental results demonstrate the effectiveness of the proposed modules. On the SCARED dataset, our model reduces the mean depth error by 12.6% compared to the baseline model and outperforms the state-of-the-art unsupervised methods and well-generalized models.

Text-to-image diffusion models have shown great potential for image editing, with techniques such as text-based and object-dragging methods emerging as key approaches. However, each of these methods has inherent limitations: text-based methods struggle with precise object positioning, while object dragging methods are confined to static relocation. To address these issues, we propose InstructUDrag, a diffusion-based framework that combines text instructions with object dragging, enabling simultaneous object dragging and text-based image editing. Our framework treats object dragging as an image reconstruction process, divided into two synergistic branches. The moving-reconstruction branch utilizes energy-based gradient guidance to move objects accurately, refining cross-attention maps to enhance relocation precision. The text-driven editing branch shares gradient signals with the reconstruction branch, ensuring consistent transformations and allowing fine-grained control over object attributes. We also employ DDPM inversion and inject prior information into noise maps to preserve the structure of moved objects. Extensive experiments demonstrate that InstructUDrag facilitates flexible, high-fidelity image editing, offering both precision in object relocation and semantic control over image content.