In order to study the application of differential privacy data release for the data platform of precise poverty alleviation (PPA), in this study, the data was protected by using differential privacy protection algorithm, and combined with artificial neural network to construct the algorithm model. And then based on MATLAB simulation experiment, the operation effect of the simulation model was verified through multiple angles. From the relationship between budget and coefficient, it can be concluded that compared with extraction procedure and noprivacy by statistical test, the algorithm proposed in this study was found to be more practical, and the result was close to the original data, and the effect was better; the error rate was also the lowest, not higher than 0.075. Comparing the accuracy of the algorithm with other algorithms, the result showed that other methods made the precision lower, but the function mechanism designed in this study did not; from the perspective of time, it is found that the time consumption of algorithm designed in this study was greatly reduced compared with other methods. Through the research in this paper, the model designed by combining artificial neural network and differential privacy achieved the expected effect. Although there are some shortcomings in the experimental process, in general, it can provide direction and guidance for the subsequent PPA work, and its social development has important guiding significance.
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Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. In: Studies in computational intelligence. Springer, Cham, Switzerland
Abualigah LMQ, Hanandeh ES (2015) Applying genetic algorithms to information retrieval using vector space model. Int J Comput Sci Eng Appl 5(1):19
Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73(11):4773–4795
Abualigah LM, Khader AT, Hanandeh ES, Gandomi AH (2017) A novel hybridization strategy for krill herd algorithm applied to clustering techniques. Appl Soft Comput 60:423–435
Abualigah LM, Khader AT, Hanandeh ES (2018a) Hybrid clustering analysis using improved Krill herd algorithm. Appl Intell 48:4047–4071
Abualigah LM, Khader AT, Hanandeh ES (2018b) A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125
Baker J, Fearnhead P, Fox EB, Nemeth C (2019) Control variates for stochastic gradient MCMC. Stat Comput 29(3):599–615
Baychev TG, Jivkov AP, Rabbani A et al (2019) Reliability of algorithms interpreting topological and geometric properties of porous media for pore network modelling. Transp Porous Media 128:271–301
Chen W, Quan-Haase A, Park YJ (2018a) Privacy and data management: the user and producer perspectives. Am Behav Sci 1:2. https://doi.org/10.1177/0002764218791287
Chen CS, Song HX, Yang HW (2018b) Liouville-type theorems for stable solutions of singular quasilinear elliptic equations in RN. Electron J Differ Equ 81:1–11
Dimitrakakis C, Nelson B, Zhang Z et al (2017) Differential privacy for Bayesian inference through posterior sampling. J Mach Learn Res 18(1):343–381
Hadar I, Hasson T, Ayalon O et al (2018) Privacy by designers: software developers’ privacy mindset. Empir Softw Eng 23(1):259–289
Huang L, Yang S (2018) Study on the sustainable development of targeted poverty alleviation through financial support. Curr Urban Stud 6(01):174
Jiang C, Zhang F, Li T (2018) Synchronization and antisynchronization of N-coupled fractional-order complex chaotic systems with ring connection. Math Methods Appl Sci 41(7):2625–2638
Kerber W (2016) Digital markets, data, and privacy: competition law, consumer law and data protection. J Intellect Prop Law Pract 11(11):856–866
Kersting K, Meyer U (2018) From big data to big artificial intelligence? Algorithmic challenges and opportunities of big data. Künstl Intell 32(1):3–8
Li D, Chen F, An Y (2018) Existence and multiplicity of nontrivial solutions for nonlinear fractional differential systems with p-Laplacian via critical point theory. Math Methods Appl Sci 41(8):3197–3212
Li T, Ma JF, Sun C (2019) SRDPV: secure route discovery and privacy-preserving verification in MANETs. Wirel Netw 25(4):1731–1747
Liang Z, Bao J (2018) Targeted poverty alleviation in China: segmenting small tourism entrepreneurs and effectively supporting them. J Sustain Tourism 26:1–18
Liu F (2018a) Rough maximal functions supported by subvarieties on Triebel-Lizorkin spaces. Revista de la Real Academia de Ciencias Exactas, Físicas y Naturales. Serie A. Matemáticas 112(2):593–614
Liu F (2018b) A note of littlewood-paley functions on Triebel-Lizorkin spaces. Bull Korean Math Soc 55(2):659–672
Liu S, Chen F, Wang Z (2018) Existence of global l-infinity solutions to a generalized n x n hyperbolic system of Leroux type. Acta Math Sci 38(3):889–897
Liu YN, Wang YP, Wang XF et al (2019) Privacy-preserving raw data collection without a trusted authority for IoT. Comput Netw 148:340–348
Martin KD, Murphy PE (2017) The role of data privacy in marketing. J Acad Mark Sci 45(2):135–155
Martin KD, Borah A, Palmatier RW (2017) Data privacy: effects on customer and firm performance. J Mark 81(1):36–58
Miao CQ (2018) Computing eigenpairs in augmented Krylov subspace produced by Jacobi-Davidson correction equation. J Comput Appl Math 343:363–372
Nelson DR, Lemos MC, Eakin H et al (2016) The limits of poverty reduction in support of climate change adaptation. Environ Res Lett 11(9):094011
Prasanth T (2019) Effective big data retrieval using deep learning modified neural networks. Mob Netw Appl 24(1):282–294
Sawat DD, Hegadi RS (2017) Unconstrained face detection: a deep learning and machine learning combined approach. CSI Trans ICT 5(2):195–199
Sweeney L (2002) Achieving k-anonymity privacy protection using generalization and suppression. Int J Uncertain Fuzziness Knowl Based Syst 10(05):571–588
Victor N, Lopez D, Abawajy JH (2016) Privacy models for big data: a survey. Int J Big Data Intell 3(1):61–75
Wagner I, Eckhoff D (2018) Technical privacy metrics: a systematic survey. ACM Comput Surv (CSUR) 51(3):57
Wang Y (2017) Village cadres and practical power: the governance order of local politics in targeted poverty alleviation. J Public Adm 3:3
Wang ZH, Li H, Li Q et al (2019) Towards IP geolocation with intermediate routers based on topology discovery. Cybersecurity 2:13
Yin C, Shi L, Sun R et al (2019) Improved collaborative filtering recommendation algorithm based on differential privacy protection. J Supercomput 7:1–14
Yu S (2016) Big privacy: challenges and opportunities of privacy study in the age of big data. IEEE Access 4:2751–2763
Zhao J, Liu J, Qin Z et al (2018) Privacy protection scheme based on remote anonymous attestation for trusted smart meters. IEEE Trans Smart Grid 9(4):3313–3320
Zheng LI, Lan DX, Han JINB (2017) Absorptive capacity of transfer payment from the perspective of targeted poverty alleviation: evidence from national-level poverty counties. Finance Trade Res 9:9
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Communicated by Mu-Yen Chen.
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Gao, S., Zhou, C. Differential privacy data publishing in the big data platform of precise poverty alleviation. Soft Comput 24, 8139–8147 (2020). https://doi.org/10.1007/s00500-019-04352-1
- Differential privacy
- Precise poverty alleviation
- Privacy budget
- Error rate
- Big data