arXiv subjects

Zhuo Ma

Publications and source records attributed to Zhuo Ma.

22 records · Page 2Linked to original sources

arXiv2020

Boosting Privately: Privacy-Preserving Federated Extreme Boosting for Mobile Crowdsensing

Recently, Google and other 24 institutions proposed a series of open challenges towards federated learning (FL), which include application expansion and homomorphic encryption (HE). The former aims to expand the applicable machine learning models of FL. The latter focuses on who holds the secret key when applying HE to FL. For the naive HE scheme, the server is set to master the secret key. Such a setting causes a serious problem that if the server does not conduct aggregation before decryption, a chance is left for the server to access the user's update. Inspired by the two challenges, we propose FedXGB, a federated extreme gradient boosting (XGBoost) scheme supporting forced aggregation. FedXGB mainly achieves the following two breakthroughs. First, FedXGB involves a new HE based secure aggregation scheme for FL. By combining the advantages of secret sharing and homomorphic encryption, the algorithm can solve the second challenge mentioned above, and is robust to the user dropout. Then, FedXGB extends FL to a new machine learning model by applying the secure aggregation scheme to the classification and regression tree building of XGBoost. Moreover, we conduct a comprehensive theoretical analysis and extensive experiments to evaluate the security, effectiveness, and efficiency of FedXGB. The results indicate that FedXGB achieves less than 1% accuracy loss compared with the original XGBoost, and can provide about 23.9% runtime and 33.3% communication reduction for HE based model update aggregation of FL.

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arXiv2020

Droidetec: Android Malware Detection and Malicious Code Localization through Deep Learning

Android malware detection is a critical step towards building a security credible system. Especially, manual search for the potential malicious code has plagued program analysts for a long time. In this paper, we propose Droidetec, a deep learning based method for android malware detection and malicious code localization, to model an application program as a natural language sequence. Droidetec adopts a novel feature extraction method to derive behavior sequences from Android applications. Based on that, the bi-directional Long Short Term Memory network is utilized for malware detection. Each unit in the extracted behavior sequence is inventively represented as a vector, which allows Droidetec to automatically analyze the semantics of sequence segments and eventually find out the malicious code. Experiments with 9616 malicious and 11982 benign programs show that Droidetec reaches an accuracy of 97.22% and an F1-score of 98.21%. In all, Droidetec has a hit rate of 91% to properly find out malicious code segments.

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arXiv2019

Revocable Federated Learning: A Benchmark of Federated Forest

A learning federation is composed of multiple participants who use the federated learning technique to collaboratively train a machine learning model without directly revealing the local data. Nevertheless, the existing federated learning frameworks have a serious defect that even a participant is revoked, its data are still remembered by the trained model. In a company-level cooperation, allowing the remaining companies to use a trained model that contains the memories from a revoked company is obviously unacceptable, because it can lead to a big conflict of interest. Therefore, we emphatically discuss the participant revocation problem of federated learning and design a revocable federated random forest (RF) framework, RevFRF, to further illustrate the concept of revocable federated learning. In RevFRF, we first define the security problems to be resolved by a revocable federated RF. Then, a suite of homomorphic encryption based secure protocols are designed for federated RF construction, prediction and revocation. Through theoretical analysis and experiments, we show that the protocols can securely and efficiently implement collaborative training of an RF and ensure that the memories of a revoked participant in the trained RF are securely removed.

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arXiv2019

Challenges in Covert Wireless Communications with Active Warden on AWGN channels

Covert wireless communication or low probability of detection (LPD) communication that employs the noise or jamming signals as the cover to hide user's information can prevent a warden Willie from discovering user's transmission attempts. Previous work on this problem has typically assumed that the warden is static and has only one antenna, often neglecting an active warden who can dynamically adjust his/her location to make better statistic tests. In this paper, we analyze the effect of an active warden in covert wireless communications on AWGN channels and find that, having gathered samples at different places, the warden can easily detect Alice's transmission behavior via a trend test, and the square root law is invalid in this scenario. Furthermore, a more powerful warden with multiple antennas is harder to be deceived, and Willie's detection time can be greatly shortened.

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