Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized version to an aggregator who performs analyses, which protects both the users and the aggregator against private information leaks. Although LDP has attracted much research attention in recent years, the majority of existing work focuses on applying LDP to complex data and/or analysis tasks. In this paper, we point out that the fundamental problem of collecting multidimensional data under LDP has not been addressed sufficiently, and there remains much room for improvement even for basic tasks such as computing the mean value over a single numeric attribute under LDP. Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance. Then, we extend these mechanisms to multidimensional data that can contain both numeric and categorical attributes, where our mechanisms always outperform existing solutions regarding worst-case noise variance. As a case study, we apply our solutions to build an LDP-compliant stochastic gradient descent algorithm (SGD), which powers many important machine learning tasks. Experiments using real datasets confirm the effectiveness of our methods, and their advantages over existing solutions.

Recently, there have been several promising techniques developed for schedulability analysis and response time analysis for multiprocessor systems based on over-approximation. This paper contains two contributions. First, to improve the analysis precision, we apply Baruah's window analysis framework [6] to response time analysis for sporadic tasks on multiprocessor systems where the deadlines of tasks are within their periods. The crucial observation is that for global fixed priority scheduling, a response time bound of each task can be efficiently estimated by fixed-point computation without enumerating all the busy window sizes as in [6] for schedulability analysis. The technique is proven to dominate theoretically state-of-the-art techniques for response time analysis for multiprocessor systems. Our experiments also show that the technique results in significant performance improvement compared with several existing techniques for multiprocessor schedulability analysis. As the second main contribution of this paper, we extend the proposed technique to task systems with arbitrary deadlines, allowing tasks to have deadlines beyond the end of their periods. This is a non-trivial extension even for single-processor systems. To our best knowledge, this is the first work for multiprocessor systems in this setting, which involves sophisticated techniques for the characterization and computation of response time bounds.

Differential privacy (DP) is a promising scheme for releasing the results of statistical queries on sensitive data, with strong privacy guarantees against adversaries with arbitrary background knowledge. Existing studies on DP mostly focus on simple aggregations such as counts. This paper investigates the publication of DP-compliant histograms, which is an important analytical tool for showing the distribution of a random variable, e.g., hospital bill size for certain patients. Compared to simple aggregations whose results are purely numerical, a histogram query is inherently more complex, since it must also determine its structure, i.e., the ranges of the bins. As we demonstrate in the paper, a DP-compliant histogram with finer bins may actually lead to significantly lower accuracy than a coarser one, since the former requires stronger perturbations in order to satisfy DP. Moreover, the histogram structure itself may reveal sensitive information, which further complicates the problem. Motivated by this, we propose two novel algorithms, namely NoiseFirst and StructureFirst, for computing DP-compliant his-tograms. Their main difference lies in the relative order of the noise injection and the histogram structure computation steps. NoiseFirst has the additional benefit that it can improve the accuracy of an already published DP-complaint histogram computed using a na¨ıvena¨ıve method. Going one step further, we extend both solutions to answer arbitrary range queries. Extensive experiments, using several real data sets, confirm that the proposed methods output highly accurate query answers, and consistently outperform existing competitors.

Three-dimensional (3D) structures of graphene have attracted extensive interest for their practical applications, such as supercapacitors and catalyst supports. Self-assembly is a typical technique to fabricate macroscopic graphene materials integrated with various superior properties. However, an efficient and environmentally-friendly strategy is still needed. In this paper, we report a green and mild method for the synthesis of 3D architectures of graphene. This proposed method is based on the chemical reduction of graphene oxide (GO) with the aid of a range of natural phenolic acids and in situ self-assembly of graphene sheets via p-p interactions. The obtained monolithic graphene exhibits low density, super hydrophobicity, high porosity, excellent mechanical strength and electrical conductivity. These multifunctional products can be used as adsorbents for removal of oils, organic solvents and dyes from contaminated water, as well as electrode materials for supercapacitors.

Flexible freestanding electrodes are highly desired to realize wearable/flexible batteries as required for the design and production of flexible electronic devices. Here, the excellent electrochemical performance and inherent flexibility of atomically thin 2D MoS 2 along with the self-assembly properties of liquid crystalline graphene oxide (LCGO) dispersion are exploited to fabricate a porous anode for high-performance lithium ion batteries. Flexible, free-standing MoS 2 -reduced graphene oxide (MG) film with a 3D porous structure is fabricated via a facile spontaneous self-assembly process and subsequent freeze-drying. This is the first report of a one-pot self-assembly, gelation, and subsequent reduction of MoS 2 /LCGO composite to form a flexible, high performance electrode for charge storage. The gelation process occurs directly in the mixed dispersion of MoS 2 and LCGO nanosheets at a low temperature (70 °C) and normal atmosphere (1 atm). The MG film with 75 wt% of MoS 2 exhibits a high reversible capacity of 800 mAh g −1 at a current density of 100 mA g −1 . It also demonstrates excellent rate capability, and excellent cycling stability with no capacity drop over 500 charge/discharge cycles at a current density of 400 mA g −1 .