With the continued scaling of integrated circuits (ICs), IR drop analysis for on-chip power grids (PGs) is crucial but increasingly computationally demanding. Traditional numerical methods deliver high accuracy but are prohibitively time-intensive, while various machine learning (ML) methods have been introduced to alleviate these computational burdens. However, most CNN-based methods ignore the fine structure and topological information of PGs, and face interpretability or scalability issues. In this work, we propose a novel graphbased framework, IRGNN, leveraging the PG topology with the integration of numerical solutions and point clouds. Our framework applies a numerical solver, AMG-PCG, to generate rough numerical solutions as a reliable interpretability foundation for ML. Then, to capture PG topology, we regard nodes of PG as point clouds and extract point cloud features, and we introduce a novel graph structure, IRGraph. Furthermore, a novel graph-based model IRGNN is designed, incorporating a designed neighbor distance attention (NDA) layer for distanceaware PG features aggregation and graph transformer (GT) layer to capture global information. It should be noted that our framework can analyze the IR drop of each node in PG, which CNN-based methods cannot do. Experimental evaluations demonstrate that our framework achieves significantly higher accuracy than previous CNN-based approaches and numerical solvers while substantially reducing computation time.

IR Drop analysis for on-chip power grids (PGs) is vital but computationally challenging due to the rapid growth in the integrated circuit (IC) scale. Traditional numerical methods employed by current EDA software are accurate but extremely time-consuming. To achieve rapid analysis of IR drop, various machine learning (ML) methods have been introduced to address the inefficiency of numerical methods. However, the issue of interpretability or scalability has been limiting practical applications. In this work, we propose IR-Fusion, which aims to combine numerical methods with ML to achieve the trade-off and complementarity between accuracy and efficiency in static IR drop analysis. Specifically, the numerical method is used to obtain rough solutions and ML models are utilized to improve accuracy further. In our framework, an efficient numerical solver, AMG-PCG, is applied to get rough numerical solutions. Then, based on the numerical solution, the fusion of hierarchical numerical-structural information representing the multilayer structure of the PG is employed, and an Inception Attention U-Net model is designed to capture details and interaction of features at different scales. To cope with the limitations and diversity of PG designs, an augmented curriculum learning strategy is applied to the training phase. Evaluation of IR-Fusion shows that its accuracy is significantly better than previous ML-based methods while requiring considerably less iteration on solver to achieve the same accuracy compared with numerical methods.

To privately publish sensitive multimedia data in an edge network with fog devices, one of the best privacy-preserving solutions is to use differential privacy (DP) mechanisms. However, existing DP data publication mechanisms for the infinite data stream of edge networks mainly focus on publishing data with specific types of data or a set of predetermined queries. This approach is not suitable for multimedia data with numerous features that require a more flexible data publishing mechanism. In this article, we propose EdgeSyn, a novel mechanism for accurately publishing multimedia data over infinite data streams in an edge network. It allocates privacy budgets with a sliding window, adopting data synthesis mechanisms to support dynamic publishing without loss of accuracy. In more detail, EdgeSyn addresses the limitations associated with data types in prior data stream publishing approaches and introduces a privacy budget management strategy that optimally allocates budgets for the implementation of data synthesis mechanisms over an infinite data stream. The experimental results show that EdgeSyn performs well under different privacy budgets and various lengths of active windows.

Electromigration (EM) has attracted significant interest in recent years, because the current density of on-chip power delivery networks (PDNs) is always increasing. However, the EM phenomenon is affected by the randomness of the annealing process during nanofabrication, which requires more reliable statistical models for EM analysis. In this work, we propose a fast estimation method for EM nucleation time based on the random activation energy model. Experiments demonstrate that our method can accurately and efficiently analyze the nucleation time distribution under random processes, and achieve 39.1% improvement in estimation speed compared with the previous work.

As feature sizes shrink, the on-chip power grid (PG) faces serious power integrity issues, and static IR drop analysis becomes critical for PG design and optimization. Many machine learning (ML) based methods have been proposed to address the inefficiencies of traditional numerical methods. However, many previous works have ignored the problems of feature confusion and imbalance IR drop distribution. In this work, we propose novel feature augmentation and selection methods to solve the feature confusion problem and use the label distribution smoothing (LDS) technique to handle unbalanced labels. Importantly, we design a static IR drop analysis model for PG using the Attention U-Net architecture (PGAU). Furthermore, two real-world datasets are used for evaluation. Experiments show that our model outperforms baselines, with a 2.6% improvement in the correlation coefficient (CC) and a 22.2% reduction in the mean absolute error (MAE). Moreover, our model is highly transferable and performs better against never-before-seen designs.

Personalized recommendation is deemed ubiquitous. Indeed, it has been applied to several online services (e.g., E-commerce, advertising, and social media applications, to name a few). Learning unknown user preferences from user-provided data lies at the core of modern collaborative filtering recommender systems. However, there is an incentive for malicious attackers to manipulate the learned preferences, which could affect business decision making, by injecting poisoned data. In the face of such a poisoning attack, while previous works have proposed a number of defense methods succeeding in other machine learning (ML) tasks, little is effective for collaborative filtering (CF). Thereof, we present a new defense scheme called poison-tolerant collaborative filtering (PTCF), which is highly robust against poisoning attacks on collaborative filtering. Different from the defenses that remove outliers or search a min-loss subset, the PTCF scheme enables collaborative filtering on an attacked training dataset while guarantees system's availability and integrity. We evaluate extensively the PTCF scheme on a public dataset (Jester) and two real-world datasets (Movie and E-Shopping), and demonstrate that the PTCF scheme is significantly effective in providing robustness.

In private data publishing, a promising solution is generating synthetic data that enables any query on the private dataset while satisfying differential privacy. Over the past decade, researchers mainly focused on improving the query accuracy of synthetic data. However, the limitations of existing works restrict them from achieving a better trade-off between accuracy and privacy. In this paper, we propose ABSyn, a novel scheme for differentially private data synthesis. Under the Select-Measure-Generate paradigm, ABSyn has an adaptive mechanism for precisely selecting marginals and follows the batch processes. Our adaptive-batch scheme can provide a well-selected marginal set and the optimal allocation of privacy budget, which makes its synthetic data achieve high accuracy without compromising privacy. We implement an efficient prototype of ABSyn and compare it with existing works by analyzing public datasets. Experimental results show that ABSyn achieves query accuracy on synthetic datasets by a factor of$1.26\times $and efficiency by a factor of$18.60\times $over the state-of-the-art scheme on average.

How to achieve distributed differential privacy (DP) without a trusted central party is of great interest in both theory and practice. Recently, the shuffle model has attracted much attention. Unlike the local DP model in which the users send randomized data directly to the data collector/analyzer, in the shuffle model an intermediate untrusted shuffler is introduced to randomly permute the data, which have already been randomized by the users, before they reach the analyzer. The most appealing aspect is that while shuffling does not explicitly add more noise to the data, it can make privacy better. The privacy amplification effect in consequence means the users need to add less noise to the data than in the local DP model, but can achieve the same level of differential privacy. Thus, protocols in the shuffle model can provide better accuracy than those in the local DP model. What looks interesting to us is that the architecture of the shuffle model is similar to private aggregation, which has been studied for more than a decade. In private aggregation, locally randomized user data are aggregated by an intermediate untrusted aggregator. Thus, our question is whether aggregation also exhibits some sort of privacy amplification effect? And if so, how good is this “aggregation model” in comparison with the shuffle model. We conducted the first comparative study between the two, covering privacy amplification, functionalities, protocol accuracy, and practicality. The results as yet suggest that the new shuffle model does not have obvious advantages over the old aggregation model. On the contrary, protocols in the aggregation model outperform those in the shuffle model, sometimes significantly, in many aspects.

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement, i.e. events are scheduled at the reasonable time to attract maximum number of participants. Existing approaches usually focus on assigning a set of events organized by the same group to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this paper, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. Unfortunately, we find that the PETA problem is NP-hard due to the global conflict constraints on events. Thus, we propose design a greedy algorithm and two approximation algorithms to solve the PETA problem. Finally, we conduct extensive experiments on both real and synthetic datasets to test the effectiveness and efficiency of the proposed algorithms.

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement. Existing approaches usually focus on assigning a set of events organized to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this work, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. We propose a method to calculate event feasible time period based on event time prediction, and design a greedy algorithm and two approximation algorithms to solve the PETA problem. Extensive experiments on both real and synthetic datasets demonstrate that the proposed algorithms have high effectiveness and efficiency.