scholarly journals An Efficient Search Algorithm for Large Encrypted Data by Homomorphic Encryption

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 484
Author(s):  
Pyung Kim ◽  
Eunji Jo ◽  
Younho Lee
Keyword(s):  
Social Security ◽  
Large Scale ◽  
Search Algorithm ◽  
Homomorphic Encryption ◽  
Personal Data ◽  
Optimal Number ◽  
Social Security Number ◽  
Encrypted Data ◽  
Efficient Search ◽  
Computer Environment

The purpose of this study is to provide an efficient search function over a large amount of encrypted data, where the bit length of each item is several tens of bits. For this purpose, we have improved the existing hybrid homomorphic encryption by enabling the longer data items to be stored while using multiple encrypted databases and by suggesting an improved search method working on top of the multiple instances of the database. Further, we found the optimal number of databases to be needed when 40-bit information, such as social security number, is stored after encryption. Through experiments, we were able to check the existence of a given (Korean) social security number of 13 decimal digits in approximately 12 s from a database that has 10 million encrypted social security numbers over a typical personal computer environment. The outcome of this research can be used to build a large-scale, practical encrypted database in order to support the search operation. In addition, it is expected to be used as a method for providing both security and practicality to the industry dealing with credit information evaluation and personal data requiring privacy.

scholarly journals PROTECTION OF PERSONAL DATA IN SOCIAL SECURITY IN THE SETTINGS OF DIGITALIZATION

2021 ◽  
Author(s):  
Hristina Blagoycheva ◽  
Keyword(s):  
Social Security ◽  
Large Scale ◽  
Personal Data ◽  
Social Programs ◽  
Access To Information ◽  
Digital Network

Digitization increases the potential of social security for greater efficiency in the provision of new services and the deployment of large-scale social programs. However, there is a possibility that these technologies will become a prerequisite for unregulated access to information and personal data stored in the digital network. Therefore, the purpose of the report is to identify some challenges in this direction and to seek possible solutions.

scholarly journals A Cost-Benefit Analysis of Capacitor Allocation Problem in Radial Distribution Networks Using an Improved Stochastic Fractal Search Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-32
Author(s):  
Phuoc Tri Nguyen ◽  
Thi Nguyen Anh ◽  
Dieu Vo Ngoc ◽  
Tung Le Thanh
Keyword(s):  
Radial Distribution ◽  
Large Scale ◽  
Cost Benefit Analysis ◽  
Search Algorithm ◽  
Cost Benefit ◽  
Distribution Networks ◽  
Optimal Number ◽  
Solution Quality ◽  
Radial Distribution Networks ◽  
Stochastic Fractal Search

This research proposes a modified metaheuristic optimization algorithm, named as improved stochastic fractal search (ISFS), which is formed based on the integration of the quasiopposition-based learning (QOBL) and chaotic local search (CLS) schemes into the original SFS algorithm for solving the optimal capacitor placement (OCP) in radial distribution networks (RDNs). The test problem involves the determination of the optimal number, location, and size of fixed and switched capacitors at different loading conditions so that the network total yearly cost is minimized with simultaneous fulfillment of operating constraints. Also, the hourly on/off scheduling plans of switched shunt capacitors (SCs) considering a modified cost objective function are obtained. The proposed ISFS algorithm has been tested on two IEEE 69-bus and 119-bus RDNs and a practical 152-bus RDN. For clarifying the effectiveness and validation of the ISFS, the simulated results have been compared with those of other previously utilized solution approaches in the literature as well as the original SFS. From result comparison, the proposed ISFS outperforms other previous approaches regarding solution quality and statistical performance for the compared cases, especially in the complex and large-scale networks. Notably, compared with the original SFS, the proposed ISFS shows a significantly better performance in all the tested cases.

scholarly journals Influence of Wind Power on Modeling of Bidding Strategy in a Promising Power Market with a Modified Gravitational Search Algorithm

2021 ◽  
Vol 11 (10) ◽  
pp. 4438
Author(s):  
Satyendra Singh ◽  
Manoj Fozdar ◽  
Hasmat Malik ◽  
Maria del Valle Fernández Moreno ◽  
Fausto Pedro García Márquez
Keyword(s):  
Wind Power ◽  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Optimization Methods ◽  
Bidding Strategy ◽  
Wind Speed Profile ◽  
Gravitational Search ◽  
Modified Gravitational Search Algorithm

It is expected that large-scale producers of wind energy will become dominant players in the future electricity market. However, wind power output is irregular in nature and it is subjected to numerous fluctuations. Due to the effect on the production of wind power, producing a detailed bidding strategy is becoming more complicated in the industry. Therefore, in view of these uncertainties, a competitive bidding approach in a pool-based day-ahead energy marketplace is formulated in this paper for traditional generation with wind power utilities. The profit of the generating utility is optimized by the modified gravitational search algorithm, and the Weibull distribution function is employed to represent the stochastic properties of wind speed profile. The method proposed is being investigated and simplified for the IEEE-30 and IEEE-57 frameworks. The results were compared with the results obtained with other optimization methods to validate the approach.

scholarly journals A Matheuristic Approach Combining Local Search and Mathematical Programming

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Carolina Lagos ◽  
Guillermo Guerrero ◽  
Enrique Cabrera ◽  
Stefanie Niklander ◽  
Franklin Johnson ◽  
...  
Keyword(s):  
Mathematical Programming ◽  
Local Search ◽  
Large Scale ◽  
Search Algorithm ◽  
Facility Location Problem ◽  
Specific Information ◽  
Local Search Algorithm ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location ◽  
Installation Cost

A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP). The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.

Bilateral bidding strategy in joint day-ahead energy and reserve electricity markets considering techno-economic-environmental measures

2021 ◽  
pp. 0958305X2110148
Author(s):  
Mojtaba Shivaie ◽  
Mohammad Kiani-Moghaddam ◽  
Philip D Weinsier
Keyword(s):  
Electricity Market ◽  
Large Scale ◽  
Search Algorithm ◽  
Power Grid ◽  
Mathematical Formulation ◽  
Test System ◽  
Bidding Strategy ◽  
Level Problem ◽  
Optimal Bidding ◽  
Environmental Measures

In this study, a new bilateral equilibrium model was developed for the optimal bidding strategy of both price-taker generation companies (GenCos) and distribution companies (DisCos) that participate in a joint day-ahead energy and reserve electricity market. This model, from a new perspective, simultaneously takes into account such techno-economic-environmental measures as market power, security constraints, and environmental and loss considerations. The mathematical formulation of this new model, therefore, falls into a nonlinear, two-level optimization problem. The upper-level problem maximizes the quadratic profit functions of the GenCos and DisCos under incomplete information and passes the obtained optimal bidding strategies to the lower-level problem that clears a joint day-ahead energy and reserve electricity market. A locational marginal pricing mechanism was also considered for settling the electricity market. To solve this newly developed model, a competent multi-computational-stage, multi-dimensional, multiple-homogeneous enhanced melody search algorithm (MMM-EMSA), referred to as a symphony orchestra search algorithm (SOSA), was employed. Case studies using the IEEE 118-bus test system—a part of the American electrical power grid in the Midwestern U.S.—are provided in this paper in order to illustrate the effectiveness and capability of the model on a large-scale power grid. According to the simulation results, several conclusions can be drawn when comparing the unilateral bidding strategy: the competition among GenCos and DisCos facilitates; the improved performance of the electricity market; mitigation of the polluting atmospheric emission levels; and, the increase in total profits of the GenCos and DisCos.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

scholarly journals Limits of data anonymity: lack of public awareness risks trust in health system activities

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Felix Gille ◽  
Caroline Brall
Keyword(s):  
Large Scale ◽  
Public Awareness ◽  
Personal Data ◽  
Public Trust ◽  
Online News ◽  
Healthcare Management ◽  
Contact Tracing ◽  
Common Denominator ◽  
The Public ◽  
Privacy And Anonymity

AbstractPublic trust is paramount for the well functioning of data driven healthcare activities such as digital health interventions, contact tracing or the build-up of electronic health records. As the use of personal data is the common denominator for these healthcare activities, healthcare actors have an interest to ensure privacy and anonymity of the personal data they depend on. Maintaining privacy and anonymity of personal data contribute to the trustworthiness of these healthcare activities and are associated with the public willingness to trust these activities with their personal data. An analysis of online news readership comments about the failed care.data programme in England revealed that parts of the public have a false understanding of anonymity in the context of privacy protection of personal data as used for healthcare management and medical research. Some of those commenting demanded complete anonymity of their data to be willing to trust the process of data collection and analysis. As this demand is impossible to fulfil and trust is built on a false understanding of anonymity, the inability to meet this demand risks undermining public trust. Since public concerns about anonymity and privacy of personal data appear to be increasing, a large-scale information campaign about the limits and possibilities of anonymity with respect to the various uses of personal health data is urgently needed to help the public to make better informed choices about providing personal data.

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