Synthetic data

Synthetic data is 'any production data applicable to a given situation that are not obtained by direct measurement' according to the McGraw-Hill Dictionary of Scientific and Technical Terms; where Craig S. Mullins, an expert in data management, defines production data as 'information that is persistently stored and used by professionals to conduct business processes.'. Synthetic data is 'any production data applicable to a given situation that are not obtained by direct measurement' according to the McGraw-Hill Dictionary of Scientific and Technical Terms; where Craig S. Mullins, an expert in data management, defines production data as 'information that is persistently stored and used by professionals to conduct business processes.'. The creation of synthetic data is an involved process of data anonymization; that is to say that synthetic data is a subset of anonymized data. Synthetic data is used in a variety of fields as a filter for information that would otherwise compromise the confidentiality of particular aspects of the data. Many times the particular aspects come about in the form of human information (i.e. name, home address, IP address, telephone number, social security number, credit card number, etc.). Synthetic data are generated to meet specific needs or certain conditions that may not be found in the original, real data. This can be useful when designing any type of system because the synthetic data are used as a simulation or as a theoretical value, situation, etc. This allows us to take into account unexpected results and have a basic solution or remedy, if the results prove to be unsatisfactory. Synthetic data are often generated to represent the authentic data and allows a baseline to be set. Another use of synthetic data is to protect privacy and confidentiality of authentic data. As stated previously, synthetic data is used in testing and creating many different types of systems; below is a quote from the abstract of an article that describes a software that generates synthetic data for testing fraud detection systems that further explains its use and importance.'This enables us to create realistic behavior profiles for users and attackers. The data is used to train the fraud detection system itself, thus creating the necessary adaptation of the system to a specific environment.' The history of the generation of synthetic data dates back to 1993. In 1993, the idea of original fully synthetic data was created by Rubin. Rubin originally designed this to synthesize the Decennial Census long form responses for the short form households. He then released samples that did not include any actual long form records - in this he preserved anonymity of the household. Later that year, the idea of original partially synthetic data was created by Little. Little used this idea to synthesize the sensitive values on the public use file. In 1994, Fienberg came up with the idea of critical refinement, in which he used a parametric posterior predictive distribution (instead of a Bayes bootstrap) to do the sampling. Later, other important contributors to the development of synthetic data generation were Trivellore Raghunathan, Jerry Reiter, Donald Rubin, John M. Abowd, and Jim Woodcock. Collectively they came up with a solution for how to treat partially synthetic data with missing data. Similarly they came up with the technique of Sequential Regression Multivariate Imputation. Synthetic data are used in the process of data mining. Testing and training fraud detection systems, confidentiality systems and any type of system is devised using synthetic data. As described previously, synthetic data may seem as just a compilation of “made up” data, but there are specific algorithms and generators that are designed to create realistic data. This synthetic data assists in teaching a system how to react to certain situations or criteria. Researcher doing clinical trials or any other research may generate synthetic data to aid in creating a baseline for future studies and testing. For example, intrusion detection software is tested using synthetic data. This data is a representation of the authentic data and may include intrusion instances that are not found in the authentic data. The synthetic data allows the software to recognize these situations and react accordingly. If synthetic data was not used, the software would only be trained to react to the situations provided by the authentic data and it may not recognize another type of intrusion. Synthetic data is also used to protect the privacy and confidentiality of a set of data. Real data contains personal/private/confidential information that a programmer, software creator or research project may not want to be disclosed. Synthetic data holds no personal information and cannot be traced back to any individual; therefore, the use of synthetic data reduces confidentiality and privacy issues. Researchers test the framework on synthetic data, which is 'the only source of ground truth on which they can objectively assess the performance of their algorithms'. Synthetic data can be generated through the use of random lines, having different orientations and starting positions. Datasets can be get fairly complicated. A more complicated dataset can be generated by using a synthesizer build. To create a synthesizer build, first use the original data to create a model or equation that fits the data the best. This model or equation will be called a synthesizer build. This build can be used to generate more data.