000			01974nam a22002057a 4500
008			220826b2020 |||||||| |||| 00| 0 eng d
020			_a9788194435013
082			_a006.312 El-K
100			_aEl Emam, Khaled
245			_aPractical synthetic data generation : _bbalancing privacy and the broad availability of data / _cKhaled El Emam, Lucy Mosquera and Richard Hoptroff
260			_aIndia _bSPD _c2020
300			_a151 p.
365			_aINR _b675.00.
500			_aBuilding and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data—fake data generated from real data—so you can perform secondary analysis to research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. This book describes: * Steps for generating synthetic data using multivariate normal distributions * Methods for distribution fitting covering different goodness-of-fit metrics * How to replicate the simple structure of the original data * An approach for modelling data structure to consider complex relationships * Multiple approaches and metrics you can use to assess data utility * How analysis performed on real data can be replicated with synthetic data * Privacy implications of synthetic data and methods to assess identity disclosure
650			_aComputer simulation
650			_aData mining
650			_aElectronic data processing
650			_aAcquisition of data sets
650			_aData protection
999			_c80382 _d80382