Anh T. Pham

Hung K. Hoang, Chuyen T. Nguyen, Thang K. Nguyen et al.

This paper investigates the secrecy sum rate (SSR) of rate-splitting multiple access (RSMA)-based visible light communication (VLC) systems considering internal eavesdropping, where legitimate users may intercept private data intended for others. We formulate an optimization problem to maximize the SSR of the system, which is inherently non-convex due to the complex coupling of the objective function and constraints. To this end, two different approaches based on the convex-concave procedure (CCCP) and semidefinite relaxation (SDR) are leveraged to solve the non-convex parameterized problem. A central focus of this work is the investigation of channel similarity (CS), which serves as a metric for quantifying spatial correlation, and its impact on SSR performance. To mitigate the performance degradation caused by high spatial correlation, we propose a channel similarity reduction (CSR) clustering strategy that proactively minimizes CS to restore the system's degrees of freedom (DoF). Numerical results are provided to demonstrate the performance of the two proposed algorithms under various levels of CS. More importantly, the findings reveal that our proposed CSR-clustering strategy significantly outperforms existing baselines, effectively overcoming the secrecy performance ceiling caused by high spatial correlation.

Anh T. Pham, Shalini Ghosh, Vinod Yegneswaran

We study the problem of data release with privacy, where data is made available with privacy guarantees while keeping the usability of the data as high as possible --- this is important in health-care and other domains with sensitive data. In particular, we propose a method of masking the private data with privacy guarantee while ensuring that a classifier trained on the masked data is similar to the classifier trained on the original data, to maintain usability. We analyze the theoretical risks of the proposed method and the traditional input perturbation method. Results show that the proposed method achieves lower risk compared to the input perturbation, especially when the number of training samples gets large. We illustrate the effectiveness of the proposed method of data masking for privacy-sensitive learning on $12$ benchmark datasets.

Hung. T Le, Hai N. Nguyen, Nam Pham Ngoc et al.

Recently, HTTP streaming has become very popular for delivering video over the Internet. For adaptivity, a provider should generate multiple versions of a video as well as the related metadata. Various adaptation methods have been proposed to support a streaming client in coping with strong bandwidth variations. However, most of existing methods target at constant bitrate (CBR) videos only. In this paper, we present a new method for quality adaptation in on-demand streaming of variable bitrate (VBR) videos. To cope with strong variations of VBR bitrate, we use a local average bitrate as the representative bitrate of a version. A buffer-based algorithm is then proposed to conservatively adapt video quality. Through experiments, we show that our method can provide quality stability as well as buffer stability even under very strong variations of bandwidth and video bitrates.

Anh T. Pham, Raviv Raich, Xiaoli Z. Fern

Labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single bag label. Computer algorithms are then used to infer the label for each instance in a bag, a process referred to as instance annotation. This task is challenging due to the ambiguity regarding the instance labels. We propose a discriminative probabilistic model for the instance annotation problem and introduce an expectation maximization framework for inference, based on the maximum likelihood approach. For many probabilistic approaches, brute-force computation of the instance label posterior probability given its bag label is exponential in the number of instances in the bag. Our key contribution is a dynamic programming method for computing the posterior that is linear in the number of instances. We evaluate our methods using both benchmark and real world data sets, in the domain of bird song, image annotation, and activity recognition. In many cases, the proposed framework outperforms, sometimes significantly, the current state-of-the-art MIML learning methods, both in instance label prediction and bag label prediction.