6 research outputs found
Global Differential Privacy for Distributed Metaverse Healthcare Systems
Metaverse-enabled digital healthcare systems are expected to exploit an
unprecedented amount of personal health data, while ensuring that sensitive or
private information of individuals are not disclosed. Machine learning and
artificial intelligence (ML/AI) techniques can be widely utilized in metaverse
healthcare systems, such as virtual clinics and intelligent consultations. In
such scenarios, the key challenge is that data privacy laws might not allow
virtual clinics to share their medical data with other parties. Moreover,
clinical AI/ML models themselves carry extensive information about the medical
datasets, such that private attributes can be easily inferred by malicious
actors in the metaverse (if not rigorously privatized). In this paper, inspired
by the idea of "incognito mode", which has recently been developed as a
promising solution to safeguard metaverse users' privacy, we propose global
differential privacy for the distributed metaverse healthcare systems. In our
scheme, a randomized mechanism in the format of artificial "mix-up" noise is
applied to the federated clinical ML/AI models before sharing with other peers.
This way, we provide an adjustable level of distributed privacy against both
the malicious actors and honest-but curious metaverse servers. Our evaluations
on breast cancer Wisconsin dataset (BCWD) highlight the privacy-utility
trade-off (PUT) in terms of diagnosis accuracy and loss function for different
levels of privacy. We also compare our private scheme with the non-private
centralized setup in terms of diagnosis accuracy
Probabilistic Constellation Shaping With Denoising Diffusion Probabilistic Models: A Novel Approach
With the incredible results achieved from generative pre-trained transformers
(GPT) and diffusion models, generative AI (GenAI) is envisioned to yield
remarkable breakthroughs in various industrial and academic domains. In this
paper, we utilize denoising diffusion probabilistic models (DDPM), as one of
the state-of-the-art generative models, for probabilistic constellation shaping
in wireless communications. While the geometry of constellations is
predetermined by the networking standards, probabilistic constellation shaping
can help enhance the information rate and communication performance by
designing the probability of occurrence (generation) of constellation symbols.
Unlike conventional methods that deal with an optimization problem over the
discrete distribution of constellations, we take a radically different
approach. Exploiting the ``denoise-and-generate'' characteristic of DDPMs, the
key idea is to learn how to generate constellation symbols out of noise,
``mimicking'' the way the receiver performs symbol reconstruction. By doing so,
we make the constellation symbols sent by the transmitter, and what is inferred
(reconstructed) at the receiver become as similar as possible. Our simulations
show that the proposed scheme outperforms deep neural network (DNN)-based
benchmark and uniform shaping, while providing network resilience as well as
robust out-of-distribution performance under low-SNR regimes and non-Gaussian
noise. Notably, a threefold improvement in terms of mutual information is
achieved compared to DNN-based approach for 64-QAM geometry.Comment: arXiv admin note: text overlap with arXiv:2309.0856
Denoising Diffusion Probabilistic Models for Hardware-Impaired Communication Systems: Towards Wireless Generative AI
Thanks to the outstanding achievements from state-of-the-art generative
models like ChatGPT and diffusion models, generative AI has gained substantial
attention across various industrial and academic domains. In this paper,
denoising diffusion probabilistic models (DDPMs) are proposed for a practical
finite-precision wireless communication system with hardware-impaired
transceivers. The intuition behind DDPM is to decompose the data generation
process over the so-called "denoising" steps. Inspired by this, a DDPM-based
receiver is proposed for a practical wireless communication scheme that faces
realistic non-idealities, including hardware impairments (HWI), channel
distortions, and quantization errors. It is shown that our approach provides
network resilience under low-SNR regimes, near-invariant reconstruction
performance with respect to different HWI levels and quantization errors, and
robust out-of-distribution performance against non-Gaussian noise. Moreover,
the reconstruction performance of our scheme is evaluated in terms of cosine
similarity and mean-squared error (MSE), highlighting more than 25 dB
improvement compared to the conventional deep neural network (DNN)-based
receivers.Comment: arXiv admin note: substantial text overlap with arXiv:2309.0856
On the privacy and security for e-health services in the metaverse: An overview
Metaverse-enabled healthcare systems are expected to efficiently utilize an unprecedented amount of health-related data without disclosing sensitive or private information of individuals during data collection, communications, and learning-based processing. In this paper, we study the privacy and security of the metaverse healthcare from different aspects. Specifically, we first investigate the security of data collection and communications in the access layer of the metaverse. We then go through the privacy and security issues inside the metaverse, by addressing the privacy and security threats of utilizing clinical machine learning for intelligent e-health. From a human-centric perspective, privacy of social interactions among patients in a metaverse-enabled healthcare platform is also studied. We try to provide a holistic and less-investigated approach to help metaverse-as-a-health service providers facilitate the realization of secure and private e-health services from different aspects, ranging from the access layer to the social interactions among clients. Future vision and directions are also discussed to bring further insights for the network designers in the metaverse era
Global Differential Privacy for Distributed Metaverse Healthcare Systems
Metaverse-enabled digital healthcare systems are expected to exploit an unprecedented amount of personal health data, while ensuring that sensitive or private information of individuals are not disclosed. Machine learning and artificial intelligence (ML/AI) techniques can be widely utilized in metaverse healthcare systems, such as virtual clinics and intelligent consultations. In such scenarios, the key challenge is that data privacy laws might not allow virtual clinics to share their medical data with other parties. Moreover, clinical AI/ML models themselves carry extensive information about the medical datasets, such that private attributes can be easily inferred by malicious actors in the metaverse (if not rigorously privatized). In this paper, inspired by the idea of “incognito mode”, which has recently been developed as a promising solution to safeguard metaverse users\u27 privacy, we propose global differential privacy for the distributed metaverse healthcare systems. In our scheme, a randomized mechanism in the format of artificial “mix-up” noise is applied to the federated clinical ML/AI models before sharing with other peers. This way, we provide an adjustable level of distributed privacy against both the malicious actors and honest-but-curious metaverse servers. Our evaluations on breast cancer Wisconsin dataset (BCWD) highlight the privacy-utility trade-off (PUT) in terms of diagnosis accuracy and loss function for different levels of privacy. We also compare our private scheme with the non-private centralized setup in terms of diagnosis accuracy
On the physical layer security of the cooperative rate-splitting aided downlink in UAV networks
Unmanned Aerial Vehicles (UAVs) have found compelling applications in intelligent logistics, search and rescue as well as in air-borne Base Station (BS). However, their communications are prone to both channel errors and eavesdropping. Hence, we investigate the max-min secrecy fairness of UAV-aided cellular networks, in which Cooperative Rate-Splitting (CRS) aided downlink transmissions are employed by each multi-antenna UAV Base Station (UAV-BS) to safeguard the downlink of a two-user Multi-Input Single-Output (MISO) system against an external multi-antenna Eavesdropper (Eve). Realistically, only Imperfect Channel State Information (ICSI) is assumed to be available at the transmitter. Additionally, we consider a realistic total power constraint and guarantee the specific Quality of Service (QoS) requirements of the legitimate users. To handle the worst-case channel uncertainty of the legitimate users and an external Eve, we conceive a robust secure resource allocation algorithm, which maximizes the minimum worst-case secrecy rate of the legitimate users. Based on the CRS principle, the transmitter splits and encodes the messages of legitimate users into common as well as private streams and the user having stronger CSI is asked to help the cell-edge user by opportunistically forwarding its decoded common message. In contrast to the existing schemes adopted in the literature for ensuring secure transmission of the first cooperative phase only, in our proposed solution the common message has a twin-fold mission. Explicitly, apart from serving as the desired message, it also acts as Artificial Noise (AN) for drowning out Eve without consuming extra power. This is in stark contrast to the conventional AN designs. In the second phase, the pure AN is directed towards the Eve, deploying a robust Maximum Ratio Transmitter (MRT) beamformer at the UAV-BS. To solve the resultant non-convex optimization problem we resort to the Sequential Parametric Convex Approximation (SPCA)