Big data analytics has been regarded as a promising technology to yield better insights into future development by government and industry. Data collection and aggregation are necessary steps to enable data analysis. However, data may be dispersed across multiple places and in different formats. Even worse, data can be encrypted under different encryption mechanisms when different data owners try to secure their data. This makes data aggregation very difficult, if not impossible, especially when the encryption keys cannot be shared for various reasons. In this paper, we take the first step in addressing this problem. More specifically, we propose a new notion of cross-domain encryption switching that bridges two well-studied encryption mechanisms, namely traditional public key encryption and identity-based encryption. As of independent interest, our notion supports keyword search over encrypted data. We provide a provably-secure instantiation satisfying the notion, and further present the efficiency analysis to show the scalability. Our proposed scheme is applicable in multi-domain cloud storage system.@en

Coronavirus disease-2019 (COVID-19) poses a significant threat to the population and urban sustainability worldwide. The surge mitigation is complicated and associates many factors, including the pandemic status, policy, socioeconomics and resident behaviours. Modelling and analytics with spatial-temporal big urban data are required to assist the mitigation of the pandemic. This study proposes a novel perspective to analyse the spatial-temporal potential exposure risk of residents by capturing human behaviours based on spatial-temporal car park availability data. Near real-time data from 1,904 residential car parks in Singapore, a classical megacity, are collected to analyse car mobility and its spatial-temporal heat map. The implementation of the circuit breaker, a COVID-19 measure, in Singapore has reduced the mobility and heat (daily frequency of mobility) significantly at about 30.0%. It contributes to a 44.3%–55.4% reduction in the transportation-related air emissions under two scenarios of travelling distance reductions. Urban sustainability impacts in both environment and economy are discussed. The spatial-temporal potential exposure risk mapping with space-time interactions is further investigated via an extended Bayesian spatial-temporal regression model. The maximal reduction rate of the defined potential exposure risk lowers to 37.6% by comparison with its peak value. The big data analytics of changes in car mobility behaviour and the resultant potential exposure risks can provide insights to assist in (a) designing a flexible circuit breaker exit strategy, (b) precise management via identifying and tracing hotspots on the mobility heat map, and (c) making timely decisions by fitting curves dynamically in different phases of COVID-19 mitigation. The proposed method has the potential to be used by decision-makers worldwide with available data to make flexible regulations and planning.@en

In this paper, the fabrication and thermal conductivity (TC) of water-based nanofluids using boron (B)-doped SiC as dispersions are reported. Doping B into the β-SiC phase leads to the shrinkage of the SiC lattice due to the substitution of Si atoms (0.134 nm radius) by smaller B atoms (0.095 nm radius). The presence of B in the SiC phase also promotes crystallization and grain growth of obtained particles. The tailored crystal structure and morphology of B-doped SiC nanoparticles are beneficial for the TC improvement of the nanofluids by using them as dispersions. Using B-doped SiC nanoparticles as dispersions for nanofluids, a remarkable improvement in stability was achieved in SiC-B6 nanofluid at pH 11 by means of the Zeta potential measurement. By dispersing B-doped SiC nanoparticles in water-based fluids, the TC of the as-prepared nanofluids containing only 0.3 vol.% SiC-B6 nanoparticles is remarkably raised to 39.3% at 30 °C compared to the base fluids, and is further enhanced with the increased temperature. The main reasons for the improvement in TC of SiC-B6 nanofluids are more stable dispersion and intensive charge ions vibration around the surface of nanoparticles as well as the enhanced TC of the SiC-B dispersions.@en

Blockchain-based distributed storage enables users to share data without the help of a centralized service provider. Decentralization eliminates traditional data loss brought by compromising the provider, but incurs the possible privacy leakage in a way that the supplier directly links the retrieved data to its ciphertext. Oblivious keyword search (OKS) has been regarded as a solution to this issue. OKS allows a user to retrieve the data associated with a chosen keyword in an oblivious way. That is, the chosen keyword and the corresponding ciphertext are unknown to the data supplier. But if the retrieval privilege is with an authorized keyword set, OKS is unavailable due to one-keyword restriction and public key encryption with keyword search (PEKS) might lead to high bandwidth consumption. In this paper, we introduce Searchain, a blockchain-based keyword search system. It enables oblivious search over an authorized keyword set in the decentralized storage. Searchain is built on top of a novel primitive called oblivious keyword search with authorization (OKSA), which provides the guarantee of keyword authorization besides oblivious search. We instantiate a provably secure OKSA scheme, featured with one-round interaction and constant size communication cost in the transfer phase. We apply OKSA and ordered multisignatures (OMS) to present a Searchain protocol, which achieves oblivious peer-to-peer retrieval with order-preserving transaction. The analysis and evaluation show that Searchain maintains reasonable cost without loss of retrieval privacy, and hence guarantees its practicality.@en

Cell-line screens create expansive datasets for learning predictive markers of drug response, but these models do not readily translate to the clinic with its diverse contexts and limited data. In the present study, we apply a recently developed technique, few-shot machine learning, to train a versatile neural network model in cell lines that can be tuned to new contexts using few additional samples. The model quickly adapts when switching among different tissue types and in moving from cell-line models to clinical contexts, including patient-derived tumor cells and patient-derived xenografts. It can also be interpreted to identify the molecular features most important to a drug response, highlighting critical roles for RB1 and SMAD4 in the response to CDK inhibition and RNF8 and CHD4 in the response to ATM inhibition. The few-shot learning framework provides a bridge from the many samples surveyed in high-throughput screens (n-of-many) to the distinctive contexts of individual patients (n-of-one).@en