arXiv subjects

Yizhi Ren

Publications and source records attributed to Yizhi Ren.

4 recordsLinked to original sources

arXiv2025

Breaking Isolation: A New Perspective on Hypervisor Exploitation via Cross-Domain Attacks

Hypervisors are under threat by critical memory safety vulnerabilities, with pointer corruption being one of the most prevalent and severe forms. Existing exploitation frameworks depend on identifying highly-constrained structures in the host machine and accurately determining their runtime addresses, which is ineffective in hypervisor environments where such structures are rare and further obfuscated by Address Space Layout Randomization (ASLR). We instead observe that modern virtualization environments exhibit weak memory isolation -- guest memory is fully attacker-controlled yet accessible from the host, providing a reliable primitive for exploitation. Based on this observation, we present the first systematic characterization and taxonomy of Cross-Domain Attacks (CDA), a class of exploitation techniques that enable capability escalation through guest memory reuse. To automate this process, we develop a system that identifies cross-domain gadgets, matches them with corrupted pointers, synthesizes triggering inputs, and assembles complete exploit chains. Our evaluation on 15 real-world vulnerabilities across QEMU and VirtualBox shows that CDA is widely applicable and effective.

cs.CR↗

arXiv2024

Towards Personal Data Sharing Autonomy:A Task-driven Data Capsule Sharing System

Personal data custodian services enable data owners to share their data with data consumers in a convenient manner, anytime and anywhere. However, with data hosted in these services being beyond the control of the data owners, it raises significant concerns about privacy in personal data sharing. Many schemes have been proposed to realize fine-grained access control and privacy protection in data sharing. However, they fail to protect the rights of data owners to their data under the law, since their designs focus on the management of system administrators rather than enhancing the data owners' privacy. In this paper, we introduce a novel task-driven personal data sharing system based on the data capsule paradigm realizing personal data sharing autonomy. It enables data owners in our system to fully control their data, and share it autonomously. Specifically, we present a tamper-resistant data capsule encapsulation method, where the data capsule is the minimal unit for independent and secure personal data storage and sharing. Additionally, to realize selective sharing and informed-consent based authorization, we propose a task-driven data sharing mechanism that is resistant to collusion and EDoS attacks. Furthermore, by updating parts of the data capsules, the permissions granted to data consumers can be immediately revoked. Finally, we conduct a security and performance analysis, proving that our scheme is correct, sound, and secure, as well as revealing more advantageous features in practicality, compared with the state-of-the-art schemes.

cs.CR↗

arXiv2024

Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion Model

Model inversion attacks (MIAs) aim to recover private data from inaccessible training sets of deep learning models, posing a privacy threat. MIAs primarily focus on the white-box scenario where attackers have full access to the model's structure and parameters. However, practical applications are usually in black-box scenarios or label-only scenarios, i.e., the attackers can only obtain the output confidence vectors or labels by accessing the model. Therefore, the attack models in existing MIAs are difficult to effectively train with the knowledge of the target model, resulting in sub-optimal attacks. To the best of our knowledge, we pioneer the research of a powerful and practical attack model in the label-only scenario. In this paper, we develop a novel MIA method, leveraging a conditional diffusion model (CDM) to recover representative samples under the target label from the training set. Two techniques are introduced: selecting an auxiliary dataset relevant to the target model task and using predicted labels as conditions to guide training CDM; and inputting target label, pre-defined guidance strength, and random noise into the trained attack model to generate and correct multiple results for final selection. This method is evaluated using Learned Perceptual Image Patch Similarity as a new metric and as a judgment basis for deciding the values of hyper-parameters. Experimental results show that this method can generate similar and accurate samples to the target label, outperforming generators of previous approaches.

cs.AI↗

arXiv2017

DeepTFP: Mobile Time Series Data Analytics based Traffic Flow Prediction

Traffic flow prediction is an important research issue to avoid traffic congestion in transportation systems. Traffic congestion avoiding can be achieved by knowing traffic flow and then conducting transportation planning. Achieving traffic flow prediction is challenging as the prediction is affected by many complex factors such as inter-region traffic, vehicles' relations, and sudden events. However, as the mobile data of vehicles has been widely collected by sensor-embedded devices in transportation systems, it is possible to predict the traffic flow by analysing mobile data. This study proposes a deep learning based prediction algorithm, DeepTFP, to collectively predict the traffic flow on each and every traffic road of a city. This algorithm uses three deep residual neural networks to model temporal closeness, period, and trend properties of traffic flow. Each residual neural network consists of a branch of residual convolutional units. DeepTFP aggregates the outputs of the three residual neural networks to optimize the parameters of a time series prediction model. Contrast experiments on mobile time series data from the transportation system of England demonstrate that the proposed DeepTFP outperforms the Long Short-Term Memory (LSTM) architecture based method in prediction accuracy.

cs.LG↗