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Local differential privacy (LDP), which perturbs the data of each user locally and only sends the noisy version of her information to the aggregator, is a popular privacy-preserving data collection mechanism. In LDP, the data collector could obtain accurate statistics without access to original data, thus guaranteeing privacy. However, a primary drawback of LDP is its disappointing utility in high-dimensional space. Although various LDP schemes have been proposed to reduce perturbation, they share the same and naive aggregation mechanism at the side of the collector. In this paper, we first bring forward an analytical framework to generally measure the utilities of LDP mechanisms in high-dimensional space, which can benchmark existing and future LDP mechanisms without conducting any experiment. Based on this, the framework further reveals that the naive aggregation is sub-optimal in high-dimensional space, and there is much room for improvement. Motivated by this, we present a re-calibration protocol HDR4ME for high-dimensional mean estimation, which improves the utilities of existing LDP mechanisms without making any change to them. Both theoretical analysis and extensive experiments confirm the generality and effectiveness of our framework and protocol., This paper is accepted and will appear in ICDE 2022 as a regular research paper

An SF6 alternative gas, C 4 F 7 N mixed with CO 2 , has attracted great attention in recent years. The related experimental reports show that it has good insulation, thermal dissipation, switching performances and other characteristics. In theoretical research area, the thermophysical properties of plasma are of great significance to study the arc behaviour. However, for C 4 F 7 N, due to its complicated molecular structure, the decomposition process during the arc process is still unknown. Also for the particles generated as the temperature increases, the spectral parameters are not available. Therefore, the studies on the thermophysical property of C 4 F 7 N are rarely reported. In order to obtain the decomposition process, we assumed that the decomposition of C 4 F 7 N molecules is in the form of covalent bond cleavage. Then the main decomposition path and decomposition products of C 4 F 7 N can be obtained by analyzing the molecular bond energy. The relevant parameters of gas particles were calculated from the molecular structure using computational chemistry software. Based on the composition results assuming local thermodynamic equilibrium, the transport properties including viscosity, thermal conductivity and electrical conductivity of C 4 F 7 N-CO 2 were calculated in the temperature range from 300 to 30000 K and at the pressures between 0.1 and 16 atm, using Chapman enskog method. For the collision integrals, the phenomenological potential was adopted.

Flexible job shop scheduling is an NP-hard problem. In this paper, we design a novel hybrid cloud particle swarm optimization (HCPSO) algorithm with genetic algorithm (GA) that is adopted to provide optimal solutions according to the pareto optimality principle in solving multi-objective FJSS problem. It is aimed at minimizing completion time of jobs, total workload and maximum workload. The novelty of the new proposed approach is that the whole particles are divided into three different populations respectively with different weights according to the fitness value. The weight has stable tendency and randomness properties based on the cloud model, which not only improves the convergence speed, but also maintains the diversity of the population. The simulation results show that the HCPSO algorithm has the advantages of small optimization, fast convergence, high efficiency and good population diversity, which verifies the effectiveness and the feasibility of HCPSO algorithm. The results of the instance verify that HCPSO algorithm is suitable for multi-objective optimization problems.