Online video streaming has evolved into an integral component of the contemporary Internet landscape. Yet, the disclosure of user requests presents formidable privacy challenges. As users stream their preferred online videos, their requests are automatically seized by video content providers, potentially leaking users’ privacy. Unfortunately, current protection methods are not well-suited to preserving user request privacy from content providers while maintaining high-quality online video services. To tackle this challenge, we introduce a novel Privacy-Preserving Video Fetching (PPVF) framework, which utilizes trusted edge devices to pre-fetch and cache videos, ensuring the privacy of users’ requests while optimizing the efficiency of edge caching. More specifically, we design PPVF with three core components: 1)Online privacy budget scheduler, which employs a theoretically guaranteed online algorithm to select non-requested videos as candidates with assigned privacy budgets. Alternative videos are chosen by an online algorithm that is theoretically guaranteed to consider both video utilities and available privacy budgets. 2)Noisy video request generator, which generates redundant video requests (in addition to original ones) utilizing correlated differential privacy to obfuscate request privacy. 3)Online video utility predictor, which leverages federated learning to collaboratively evaluate video utility in an online fashion, aiding in video selection in 1) and noise generation in 2). Finally, we conduct extensive experiments using real-world video request traces from Tencent Video and Netflix. The results demonstrate that PPVF effectively safeguards user request privacy while upholding high video caching performance.

As users request their preferred metaversal media, e.g., 360-degree video, user requests tracked by metaverse content providers (MCPs) pose significant privacy leakage risks. Unfortunately, existing privacy-enhancing techniques are largely ineffective in protecting user privacy for metaversal content requests since these requests cannot be easily altered or concealed by users and must remain visible to MCPs to ensure accurate content delivery. To safeguard user privacy in metaverse multimedia services (MMS), one practical approach is pre-fetching multimedia content (e.g., short videos, video patches in 360$^\circ$videos) that is not directly related to users' interests, thereby preventing MCPs from accurately inferring user preferences. However, plain pre-fetching strategies encounter a critical trade-off between privacy protection and edge caching performance given that MCPs often rely on edges for distributing metaverse content. In this paper, we propose acache-friendly and privacy-awarecontent pre-fetching(CRACE) algorithm for user devices (UDs) along with a complementary caching algorithm for edge caches (ECs). CRACE effectively mitigates privacy leakage in metaverse content requests while minimally impacting caching performance. Specifically, we introduce a novel privacy model to guide pre-fetching decisions and formulate a Stackelberg game to analyze strategic interactions between UDs and ECs. We derive optimal strategies that maximize their respective utilities and demonstrate the existence and uniqueness of the Stackelberg equilibrium. Extensive experiments conducted with real-world data demonstrate that CRACE significantly enhances privacy protection, reducing privacy disclosure by up to 59.03% compared to baseline algorithms, with negligible impact on the edge caching performance.

Deep learning-based semantic segmentation technology has become a critical tool in assisting doctors with automatic lesion segmentation in medical images. However, the high cost of acquiring large-scale, pixel-level annotations poses a significant challenge, limiting the scalability and application of fully supervised semantic segmentation models. To address this, weakly supervised learning-based semantic segmentation models have emerged as a promising solution. These models can accurately segment lesion regions using only weak annotations, such as image-level or frame-level labels, significantly reducing the annotation burden. This approach has gained substantial attention in current research.Among various medical imaging modalities, ultrasound imaging stands out as a primary diagnostic tool due to its rapid imaging speed, ease of use, and accessibility. This paper focuses on the study of thyroid ultrasound imaging, aiming to achieve accurate classification of nodule regions. The goal is to provide clinicians with more precise diagnostic information, improving decision-making in thyroid disease diagnosis.

To mitigate the rising concern of privacy leakage, the federated recommender (FR) paradigm emerges as a potential solution, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances the FR by injecting differentially private (DP) noises into clients' data. Yet, current DPFRs, suffering from noise distortion, cannot achieve the desired satisfactory accuracy. Various efforts have been dedicated to improving DPFRs by adaptively allocating the privacy budget over the learning process. However, due to the intricate relation between privacy budget allocation and model accuracy, existing attempts are still far from maximizing the DPFR accuracy. To address this challenge, we develop a BGTplanner (Budget Planner) to strategically allocate the privacy budget for each round of the DPFR training, improving overall training performance. Specifically, we leverage the Gaussian process regression and historical information to predict the change in the recommendation accuracy with a certain allocated privacy budget. Additionally, Contextual Multi-Armed Bandit (CMAB) is harnessed to make privacy budget allocation decisions by reconciling the current improvement and long-term privacy constraints. Our extensive experimental results on real datasets demonstrate that theBGTplannerachieves an average improvement of 6.76% in training performance compared to the state-of-the-art baselines.

The publicly released machine learning (ML) models are susceptible to malicious attacks (e.g., gradient leakage attacks), which may expose sensitive training data of data-sharing platforms to untrusted third-parties. To preserve the privacy of training data, differential privacy (DP) is exploited to limit the amount of leaked privacy with a predefined budget, which in fact is a non-recoverable resource. Considering DP, allocating privacy budgets to ML queries is a non-trivial but crucial problem because a certain amount of non-recoverable privacy budget will be consumed if a datablock is assigned to a query once. Meanwhile, both datablocks and ML queries are continuously generated, which further complicates the problem. Most existing works simply relied on greedy-based algorithms to make myopic allocation decisions, far away from the optimal decision. In this paper, we propose a novelHistory-awarePrivacyBudgetAllocation (HPBA) algorithm for data-sharing platforms to address the above challenges. Different from existing works, HPBA leverages historical query records to approximate global ML query patterns so as to overcome the drawback of shortsighted greedy-based algorithms. Moreover, the performance of HPBA is theoretically guaranteed by competitive analysis. A lightweight version called S-HPBA is proposed to further reduce computation overhead by using fewer historical records. Experimental results demonstrate that, compared to the state-of-the-art baselines, HPBA and S-HPBA improve the average performance by 32.8% and 16.2% in terms of model accuracy, respectively.

Deep learning-based thyroid ultrasound image segmentation is of great importance in clinical diagnosis. The Weakly Supervised Semantic Segmentation (WSSS) models requiring only Image-level Labels (IL) reduce the dependence on pixel-level labels. However, due to the lack of position of the objects in image-level labels, WSSS methods with IL are generally based on Class Activation Map (CAM) that can locate the objects. Nowadays, channel attention, which is one of the effective tools to extract object features from images, has been widely used in semantic segmentation tasks. Unfortunately, due to the large differences in the size and discriminative features of thyroid nodules, the mainstream channel attention is unable to perform flexible feature extraction for nodules, leading to the problem of under-segmentation or over-segmentation in the predicted results of the model. To overcome this issue, we propose a dynamic channel attention network that can extract features of thyroid nodules adaptively, called the CAM-guided Adaptive Attention Network (CAANet). In detail, CAANet can pay different attention to the overall and discriminative features of nodules based on the information provided by CAM. Finally, to verify the effectiveness of our method, we perform experimental comparisons with recent WSSS methods and the mainstream channel attention methods on the thyroid ultrasound image dataset. The evaluation results show that our method has better performance improvement, with a mean Intersection over Union (IoU) of 52.713%.

It is always a challenging problem to deliver a huge volume of videos over the Internet. To meet the high bandwidth and stringent playback demand, one feasible solution is to cache video contents on edge servers based on predicted video popularity. Traditional caching algorithms (e.g., LRU, LFU) are too simple to capture the dynamics of video popularity, especially long-tailed videos. Recent learning-driven caching algorithms (e.g., DeepCache) show promising performance, however, such black-box approaches are lack of explainability and interpretability. Moreover, the parameter tuning requires a large number of historical records, which are difficult to obtain for videos with low popularity. In this paper, we optimize video caching at the edge using a white-box approach, which is highly efficient and also completely explainable. To accurately capture the evolution of video popularity, we develop a mathematical model calledHRSmodel, which is the combination of multiple point processes, including Hawkes’ self-exciting, reactive and self-correcting processes. The key advantage of the HRS model is its explainability, and much less number of model parameters. In addition, all its model parameters can be learned automatically through maximizing the Log-likelihood function constructed by past video request events. Next, we further design an online HRS-based video caching algorithm. To verify its effectiveness, we conduct a series of experiments using real video traces collected from Tencent Video, one of the largest online video providers in China. Experiment results demonstrate that our proposed algorithm outperforms the state-of-the-art algorithms, with 15.5% improvement on average in terms of cache hit rate under realistic settings.