LoPub: High-Dimensional Crowdsourced Data Publication with Local Differential Privacy

High-dimensional crowdsourced data collected from numerous users produces rich knowledge about our society. However, it also brings unprecedented privacy threats to the participants. Local differential privacy (LDP), a variant of differential privacy, is recently proposed as a state-of-the-art privacy notion. Unfortunately, achieving LDP on high-dimensional crowdsourced data publication raises great challenges in terms of both computational efficiency and data utility. To this end, based on Expectation Maximization (EM) algorithm and Lasso regression, we first propose efficient multi-dimensional joint distribution estimation algorithms with LDP. Then, we develop a Local differentially private high-dimensional data Publication algorithm, LoPub, by taking advantage of our distribution estimation techniques. In particular, correlations among multiple attributes are identified to reduce the dimensionality of crowdsourced data, thus speeding up the distribution learning process and achieving high data utility. Extensive experiments on realworld datasets demonstrate that our multivariate distribution estimation scheme significantly outperforms existing estimation schemes in terms of both communication overhead and estimation speed. Moreover, LoPub can keep, on average, 80% and 60% accuracy over the released datasets in terms of SVM and random forest classification, respectively

LDP-IDS: Local Differential Privacy for Infinite Data Streams

Streaming data collection is essential to real-time data analytics in various IoTs and mobile device-based systems, which, however, may expose end users' privacy. Local differential privacy (LDP) is a promising solution to privacy-preserving data collection and analysis. However, existing few LDP studies over streams are either applicable to finite streams only or suffering from insufficient protection. This paper investigates this problem by proposing LDP-IDS, a novel $w$-event LDP paradigm to provide practical privacy guarantee for infinite streams at users end, and adapting the popular budget division framework in centralized differential privacy (CDP). By constructing a unified error analysi for LDP, we first develop two adatpive budget division-based LDP methods for LDP-IDS that can enhance data utility via leveraging the non-deterministic sparsity in streams. Beyond that, we further propose a novel population division framework that can not only avoid the high sensitivity of LDP noise to budget division but also require significantly less communication. Based on the framework, we also present two adaptive population division methods for LDP-IDS with theoretical analysis. We conduct extensive experiments on synthetic and real-world datasets to evaluate the effectiveness and efficiency pf our proposed frameworks and methods. Experimental results demonstrate that, despite the effectiveness of the adaptive budget division methods, the proposed population division framework and methods can further achieve much higher effectiveness and efficiency.Comment: accepted to SIGMOD'2

How Framelets Enhance Graph Neural Networks

This paper presents a new approach for assembling graph neural networks based on framelet transforms. The latter provides a multi-scale representation for graph-structured data. We decompose an input graph into low-pass and high-pass frequencies coefficients for network training, which then defines a framelet-based graph convolution. The framelet decomposition naturally induces a graph pooling strategy by aggregating the graph feature into low-pass and high-pass spectra, which considers both the feature values and geometry of the graph data and conserves the total information. The graph neural networks with the proposed framelet convolution and pooling achieve state-of-the-art performance in many node and graph prediction tasks. Moreover, we propose shrinkage as a new activation for the framelet convolution, which thresholds high-frequency information at different scales. Compared to ReLU, shrinkage activation improves model performance on denoising and signal compression: noises in both node and structure can be significantly reduced by accurately cutting off the high-pass coefficients from framelet decomposition, and the signal can be compressed to less than half its original size with well-preserved prediction performance.Comment: 24 pages, 17 figures, 8 tables, ICML202

General synthesis of transition metal oxide ultrafine nanoparticles embedded in hierarchically porous carbon nanofibers as advanced electrodes for lithium storage

A unique general, large-scale, simple, and cost-effective strategy, i.e., foaming-assisted electrospinning, for fabricating various transition metal oxides into ultrafine nanoparticles (TMOs UNPs) that are uniformly embedded in hierarchically porous carbon nanofibers (HPCNFs) has been developed. Taking advantage of the strong repulsive forces of metal azides as the pore generator during carbonization, the formation of uniform TMOs UNPs with homogeneous distribution and HPCNFs is simultaneously implemented. The combination of uniform ultrasmall TMOs UNPs with homogeneous distribution and hierarchically porous carbon nanofibers with interconnected nanostructure can effectively avoid the aggregation, dissolution, and pulverization of TMOs, promote the rapid 3D transport of both Li ions and electrons throughout the whole electrode, and enhance the electrical conductivity and structural integrity of the electrode. As a result, when evaluated as binder-free anode materials in Li-ion batteries, they displayed extraordinary electrochemical properties with outstanding reversible capacity, excellent capacity retention, high Coulombic efficiency, good rate capability, and superior cycling performance at high rates. More importantly, the present work opens up a wide horizon for the fabrication of a wide range of ultrasmall metal/metal oxides distributed in 1D porous carbon structures, leading to advanced performance and enabling their great potential for promising large-scale applications