An official website of the United States government
Encrypted voice-over-IP (VoIP) communication often uses variable bit rate (VBR) codecs to achieve good audio quality while minimizing bandwidth costs. Prior work has shown that encrypted VBR-based VoIP streams are vulnerable to re-identification attacks in which an attacker can infer attributes (e.g., the language being spoken, the identities of the speakers, and key phrases) about the underlying audio by analyzing the distribution of packet sizes. Existing defenses require the participation of both the sender and receiver to secure their VoIP communications. This paper presents Whisper, the first unilateral defense against re-identification attacks on encrypted VoIP streams. Whisper works by modifying the audio signal before it is encoded by the VBR codec, adding inaudible audio that either falls outside the fixed range of human hearing or is within the human audible range but is nearly imperceptible due to its low amplitude. By carefully inserting such noise, Whisper modifies the audio stream's distribution of packet sizes, significantly decreasing the accuracy of re-identification attacks. Its use is imperceptible by the (human) receiver. Whisper can be instrumented as an audio driver and requires no changes to existing (potentially closed-source) VoIP software. Since it is a unilateral defense, it can be applied at will by a user to enhance the privacy of its voice communications. We demonstrate that Whisper significantly reduces the accuracy of re-identification attacks and incurs only a small degradation in audio quality.
more »
« less
This content will become publicly available on June 13, 2026
Diff-GOn: Enhancing Diffusion Models for Goal-Oriented Communications
The rapid expansion of edge devices and Internet of Things (IoT) continues to heighten the demand for data transport under limited spectrum resources. The goal-oriented communications (GO-COM), unlike traditional communication systems designed for bit-level accuracy, prioritizes more critical information for specific application goals at the receiver. To improve the efficiency of generative learning models for GOCOM, this work introduces a novel noise-restricted diffusion based GO-COM (Diff-GOn) framework for reducing bandwidth overhead while preserving the media quality at the receiver. Specifically, we propose an innovative Noise-Restricted Forward Diffusion (NR-FD) framework to accelerate model training and reduce the computation burden for diffusion-based GO-COMs by leveraging a pre-sampled pseudo-random noise bank (NB). Moreover, we design an early stopping criterion for improving computational efficiency and convergence speed, allowing high quality generation in fewer training steps. Our experimental results demonstrate superior perceptual quality of data transmission at a reduced bandwidth usage and lower computation, making Diff-GOn well-suited for real-time communications and downstream applications.
more »
« less
- Award ID(s):
- 2332760
- PAR ID:
- 10637721
- Publisher / Repository:
- IEEE
- Date Published:
- Subject(s) / Keyword(s):
- Noise bank, goal-oriented communications (GOCOM), autonomous driving, denoising diffusion probabilistic model
- Format(s):
- Medium: X
- Location:
- Montreal, Canada
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
TPC of IEEE ESSCIRC Conference (Ed.)This paper presents an mmWave FMCW radar that can achieve sub-centimeter-scale range resolution at 14- GHz chirp-bandwidth while maintaining the radar range beyond 50 meters. To meet the requirements on power efficiency, chirp linearity, and signal-to-noise ratio (SNR), a phase-locked steppedchirp FMCW radar architecture is introduced. Specifically, a fully integrated radar transceiver comprising an interleaved frequency-segmented phase-locked transmitter and a segmented receiver architecture with high sensitivity is presented. The proposed design addresses the limitations of conventional typeII phase-locked loops (PLLs) in extending the radar bandwidth across multiple sub-bands with identical chirp profiles. Fabricated in a 22nm FD-SOI technology, the prototype chip comprises two sub-bands with 14 GHz of free-running bandwidth and 10 GHz of phase-locked bandwidth. The system achieves -101.7 dBc/Hz phase noise at 1 MHz offset, 8 dBm of effective isotropic radiated power (EIRP), 10 dB noise figure (NF), and 362.6 mW collective power consumption of transmitter and receiver arrays.more » « less
-
null (Ed.)Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. Distributed computation makes FL attractive for bandwidth limited applications especially in wireless communications. There can be a large number of distributed edge devices connected to a central parameter server (PS) and iteratively download/upload data from/to the PS. Due to limited bandwidth, only a subset of connected devices can be scheduled in each round. There are usually millions of parameters in the state-of-art machine learning models such as deep learning, resulting in a high computation complexity as well as a high communication burden on collecting/distributing data for training. To improve communication efficiency and make the training model converge faster, we propose a new scheduling policy and power allocation scheme using non-orthogonal multiple access (NOMA) settings to maximize the weighted sum data rate under practical constraints during the entire learning process. NOMA allows multiple users to transmit on the same channel simultaneously. The user scheduling problem is transformed into a maximum-weight independent set problem that can be solved using graph theory. Simulation results show that the proposed scheduling and power allocation scheme can help achieve a higher FL testing accuracy in NOMA based wireless networks than other existing schemes within the same learning time.more » « less
-
Diffusion models, emerging as powerful deep generative tools, excel in various applications. They operate through a two-steps process: introducing noise into training samples and then employing a model to convert random noise into new samples (e.g., images). However, their remarkable generative performance is hindered by slow training and sampling. This is due to the necessity of tracking extensive forward and reverse diffusion trajectories, and employing a large model with numerous parameters across multiple timesteps (i.e., noise levels). To tackle these challenges, we present a multi-stage framework inspired by our empirical findings. These observations indicate the advantages of employing distinct parameters tailored to each timestep while retaining universal parameters shared across all time steps. Our approach involves segmenting the time interval into multiple stages where we employ custom multi-decoder U-net architecture that blends time-dependent models with a universally shared encoder. Our framework enables the efficient distribution of computational resources and mitigates inter-stage interference, which substantially improves training efficiency. Extensive numerical experiments affirm the effectiveness of our framework, showcasing significant training and sampling efficiency enhancements on three state-of-the-art diffusion models, including large-scale latent diffusion models. Furthermore, our ablation studies illustrate the impact of two important components in our framework: (i) a novel timestep clustering algorithm for stage division, and (ii) an innovative multi-decoder U-net architecture, seamlessly integrating universal and customized hyperparameters.more » « less
-
The noise performance of a high sensitivity, wide-field astronomical phased array feed receiver can be characterized by measurements using the antenna Y factor method. These measurements are used to determine figures of merit for an active array receiver. Antenna elements for the Advanced L Band Phased Array Camera for Astronomy (ALPACA) were measured using the antenna Y factor method to determine the active array and receiver noise figure, the antenna loss, receiver equivalent noise temperature, and radiation efficiency of the system over its 500[Formula: see text]MHz operating bandwidth. The completed ALPACA instrument will feature a fully cryogenic design with both the low-noise amplifiers and array elements cryogenically cooled. The uncooled performance measurements from the antenna Y factor method are used to extrapolate the elements cryogenic radiation efficiency and antenna loss showing that it is expected that the elements will contribute less than 1 K to the overall system noise temperature. These results validate the antenna Y factor method to measure key antenna parameters such as the antenna radiation efficiency and show that the instruments front-end array and electronics meets expected performance targets.more » « less
