Elaboration and characterization of nanoplate structured alpha-Fe2O3 films by Ag3PO4

A new strategy for surface treatment of hematite nanoplates for efficient photoelectrochemical (PEC) performances is proposed. Silver orthophosphate (Ag₃PO₄) has been adopted to mediate the formation of α-Fe₂O₃ films. Phosphate ions in Ag₃PO₄ is found to cause a significant morphology change during annealing process, from β-FeOOH nanorod arrays to hematite nanoplates. Meanwhile, Ag ions is doped into α-Fe₂O₃ film. The obtained nanoplate structured Fe₂O₃ –Ag–P films demonstrate much higher photoelectrochemical performance as photoanodes than the bare Fe₂O₃ nanorod thin films. The effects of phosphate and silver ions on the morphology, surface characteristics and the PEC properties of the photoanodes are investigated

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

A novel temporal perturbation based privacy-preserving scheme for real-time monitoring systems

In real-time monitoring systems, participant's privacy could be easily exposed when the time-series of sensing measurements are obtained accurately by adversaries. To address privacy issues, a number of privacy-preserving schemes have been designed for various monitoring applications. However, these schemes either lack considerations for temporal privacy or have less resistance to filtering attacks, or cause time delay with low utility. In this paper, we introduce a lightweight temporal perturbation based scheme, where sensor readings are buffered and disordered to obfuscate the temporal information of the original sensor measurement stream with differential privacy. Besides, we design the operations on the system server side to exploit the data utility in measurements from large number of sensors. We evaluate the performance of the proposed scheme through both rigorous theoretical analysis and extensive simulation experiments in comparison with related existing schemes. Evaluation results show that the proposed scheme manages to preserve both the temporal privacy and measurement privacy with filter-resistance, and achieves better performance in terms of computational overhead, data utility of real-time aggregation, and individual accumulation

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