Model of Execution Trace Obfuscation Between Threads

Sha, Zihan; Shu, Hui; Xiong, Xiaobing; Kang, Fei

doi:10.1109/tdsc.2021.3123159

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…Second, implementing model watermarking which involves adding a unique identifier or "watermark" to the model, in order to help in identifying the source of any copied models. Third, leveraging model obfuscation [99] which involves making the model more difficult to understand or reverse-engineer. Fourth, implementing model versioning that helps the participants to keep track of versions of the model, which is helpful in implementing controls to ensure that only authorized users have access to the latest version, hence preventing the use of outdated or stolen models.…”

Section: ) Overfitting Attacksmentioning

confidence: 99%

Privacy and Security in Distributed Learning: A Review of Challenges, Solutions, and Open Research Issues

Afzal,

Abdellatif,

Zubair

et al. 2023

IEEE Access

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In recent years, the way that machine learning is used has undergone a paradigm shift driven by distributed and collaborative learning. Several approaches have emerged to enable pervasive computing and distributed learning in ubiquitous Internet of Things (IoT) systems. Numerous decentralized strategies have been proposed to deal with the limitations of centralized learning, including privacy and latency due to sharing local data, while utilizing distributed computations as a promising substitute to centralized learning. However, such distributed learning schemes come with new security and privacy concerns that should be addressed. Thus, in this paper, we first provide an overview for the emerging paradigms developed for distributed learning. Then, we performed a comprehensive survey for the privacy and security challenges associated with distributed learning along with the presented solutions to overcome them. Furthermore, we highlight key challenges and open future research directions toward implementing more robust distributed systems.INDEX TERMS Data privacy and security, Internet of Things (IoT), deep learning, adversarial attacks.

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Section: ) Overfitting Attacksmentioning

confidence: 99%

Privacy and Security in Distributed Learning: A Review of Challenges, Solutions, and Open Research Issues

Afzal,

Abdellatif,

Zubair

et al. 2023

IEEE Access

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show abstract

“…Linn et al [25] focused on the static disassembly process and proposed two algorithms to disturb the static disassembly, which failed the disassembly of executable files. Sha et al [26] protect the function calling process using multi-threaded mutually exclusive execution. e method creates multiple threads in a program.…”

Section: Introductionmentioning

confidence: 99%

COOPS: A Code Obfuscation Method Based on Obscuring Program Semantics

Li¹,

Kang²,

Hui³

et al. 2022

Security and Communication Networks

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As reverse engineering technology develops rapidly, the financial loss caused by software security issues is urgent. Therefore, how to effectively protect software is a critical problem to solve. The software protection method based on code obfuscation is an effective way, and constructing an effective obfuscation algorithm can increase the cost of reverse software. It is conspicuous that current development of code obfuscation focuses on increasing the complexity of the code structure without paying much attention to the protection of program semantic information, which may help experienced attackers improve their analysis efficiency. This paper proposes COOPS for protecting software based on program semantic information, in which functions are regarded as basic semantic units. The switch relationship between the intrafunction control flow and the interfunction calling is established. The interfunction calling can be hidden in the intrafunction control flow, and in reverse, the intrafunction control flow can also be converted to interfunction calling. In this way, considering intraprogram function semantic unit level discrete, this method reconstructs the intraprogram semantic relationship. To determine the relative effectiveness, we have evaluated COOPS on OpenSSL and SpecInt-2000 test sets. For both of them, the function calling graphs before and after obfuscation differ more than 90%, which means COOPS significantly changes the control flow of the program. The evaluation shows that compared with O-LLVM, COOPS manifests strong resistance to Asm2vec and other program similarity analysis techniques and significantly improves the level of software protection rather than necessitating time-consuming and heavyweight problems.

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Model of Execution Trace Obfuscation Between Threads

Cited by 2 publications

References 34 publications

Privacy and Security in Distributed Learning: A Review of Challenges, Solutions, and Open Research Issues

Privacy and Security in Distributed Learning: A Review of Challenges, Solutions, and Open Research Issues

COOPS: A Code Obfuscation Method Based on Obscuring Program Semantics

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