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pro vyhledávání: '"Lu, Rongxing"'
Recently, deep learning-based Image-to-Image (I2I) networks have become the predominant choice for I2I tasks such as image super-resolution and denoising. Despite their remarkable performance, the backdoor vulnerability of I2I networks has not been e
Externí odkaz:
http://arxiv.org/abs/2407.10445
The Large Language Models (LLMs) are poised to offer efficient and intelligent services for future mobile communication networks, owing to their exceptional capabilities in language comprehension and generation. However, the extremely high data and c
Externí odkaz:
http://arxiv.org/abs/2308.14367
With the emergence of privacy leaks in federated learning, secure aggregation protocols that mainly adopt either homomorphic encryption or threshold secret sharing have been widely developed for federated learning to protect the privacy of the local
Externí odkaz:
http://arxiv.org/abs/2306.08970
In order to save computing power yet enhance safety, there is a strong intention for autonomous vehicles (AVs) in future to drive collaboratively by sharing sensory data and computing results among neighbors. However, the intense collaborative comput
Externí odkaz:
http://arxiv.org/abs/2304.08875
Since Shannon's pioneering masterpiece which established the prototype of modern information theory, the goal of communications has long been promising accurate message reconstruction at a refined bit-by-bit level, which deliberately neglects the sem
Externí odkaz:
http://arxiv.org/abs/2304.00848
Considerable attention has been paid to dynamic searchable symmetric encryption (DSSE) which allows users to search on dynamically updated encrypted databases. To improve the performance of real-world applications, recent non-interactive multi-client
Externí odkaz:
http://arxiv.org/abs/2212.02859
Autor:
Ren, Hao, Xu, Guowen, Zhang, Tianwei, Ning, Jianting, Huang, Xinyi, Li, Hongwei, Lu, Rongxing
Fueled by its successful commercialization, the recommender system (RS) has gained widespread attention. However, as the training data fed into the RS models are often highly sensitive, it ultimately leads to severe privacy concerns, especially when
Externí odkaz:
http://arxiv.org/abs/2212.01537
Autor:
Xu, Guowen, Xu, Shengmin, Ning, Jianting, Zhang, Tianwei, Huang, Xinyi, Li, Hongwei, Lu, Rongxing
Sparse inner product (SIP) has the attractive property of overhead being dominated by the intersection of inputs between parties, independent of the actual input size. It has intriguing prospects, especially for boosting machine learning on large-sca
Externí odkaz:
http://arxiv.org/abs/2210.08421
Autor:
Pathmaperuma, Madushi H., Rahulamathavan, Yogachandran, Dogan, Safak, Kondoz, Ahmet M., Lu, Rongxing
Despite the widespread use of encryption techniques to provide confidentiality over Internet communications, mobile device users are still susceptible to privacy and security risks. In this paper, a new Deep Neural Network (DNN) based user activity d
Externí odkaz:
http://arxiv.org/abs/2203.15501