Introduction
Artificially created data that is intended to resemble real-world data is known as synthetic data. It is produced using statistical models and algorithms that mimic the trends, traits, and connections seen in empirical data. Among other things, synthetic data can be used to enhance privacy and security, test and improve machine learning algorithms, and lessen bias in data sets.
The uses of synthetic data are numerous. One of the most significant is that, as highlighted by GDPR compliance criteria, synthetic data can save you millions of dollars by addressing issues related to privacy and data protection. The General Data Protection Regulation is known as GDPR. The European Union (EU) has established a collection of rules and regulations that specify how businesses and other organizations must manage and safeguard the personal data of people who reside in the EU.
What is Synthetic Data?
Data that has been artificially annotated and created by computer simulations or algorithms are referred to as synthetic data. Real-world data is routinely replaced by synthetic data in research. Despite being manufactured, synthetic data statistically replicates the patterns and traits of actual data. This is a crucial feature of artificial data.
Although it can be utilized in many other applications, synthetic data is especially helpful in artificial intelligence and machine learning. It enables academics and professionals to avoid bias, incompleteness, and a lack of variation in real-world data.
Machine learning models may be trained using a variety of data sets thanks to the capacity to produce synthetic data in large quantities and with a wide range of attributes. This is especially helpful when real-world data is hard to get by or scarce.
The ability to use synthetic data to safeguard people’s privacy is one of its main applications. By removing sensitive information, such as personally identifiable information (PII), or by generating data without PII, real-world data can be converted into synthetic data. The data can then be used by researchers to develop machine learning models without endangering the privacy of individuals.
Use-cases of Synthetic Data
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Training Machine Learning Models
Machine learning model training on synthetic or artificially generated data is one of the most popular applications for synthetic data in artificial intelligence. Large amounts of real-world data are frequently difficult and time-consuming to gather, especially if the data is delicate or covered by laws like the GDPR. Additionally, real-world data may be inaccurate, partial, or biased. Synthetic data can be used to train machine learning models in place of real-world data.
Machine learning models can be trained on a broader and more varied set of data by using synthetic data to complement or replace real-world data. This could enhance the model’s functionality and generalizability.
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Reducing Bias in your Data
To improve fairness and decrease bias in data sets, synthetic data can also be employed. Machine learning models that are biased or unjust can frequently result from real-world data being unbalanced or biased.
If a data set doesn’t accurately represent the population it is intended to examine, it may be biased. It might not be a good picture of what other groups experience or act like, for instance, if the majority of the data in a collection originates from one group, such as a certain ethnicity or gender. As a result, machine learning models may end up being unrepresentative of the target population.
By enabling researchers to build data sets that more exactly reflect the population they are investigating, synthetic data can help eliminate bias in data. Researchers can manage the distribution of gender, ethnicity, and other demographic features within a data set by using synthetic data. By doing this, the data set can be made to be more representative of the intended audience.
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Enhance Privacy By Protecting Personally Identifiable Information
Increasing privacy and security is a typical use case for synthetic data. Real-world data frequently includes private or delicate information that shouldn’t be made public. This data can be represented using synthetic data in a way that protects privacy, enabling its usage for study or analysis without endangering the privacy of specific people.
PII stands for personally identifiable information, which includes names, addresses, phone numbers, email addresses, and Social Security numbers. Before collecting, using, or sharing this kind of information, organizations are required under GDPR to preserve it and obtain express consent from the individuals concerned.
General Data Protection Regulation (GDPR)
The General Data Protection Regulation is known as GDPR. It is a set of guidelines established by the European Union (EU) that specify how businesses and other organizations must manage and safeguard the personal data of people who reside in the EU. It provides people more control over their personal information and holds companies responsible for data breaches or the misuse of customer information.
The GDPR has severe penalties for non-compliance. If the rules are broken, organizations could be penalized up to 4% of their annual global revenue. This is meant to discourage companies from not taking data privacy seriously. Additionally, businesses that abuse customers’ data may be liable to administrative fines and penalties, and customers may sue such businesses in court.
Understanding pseudonymization and anonymization, two key underlying principles, is essential before moving on to the use case. By deleting or hiding personally identifying information (PII) from data sets, pseudonymization, and anonymization are two strategies used to safeguard individuals’ privacy. The two methods do differ in several significant ways, though.
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Pseudonymization
Pseudonymization is the process of replacing private data with a pseudonym or other made-up identification, such as names and addresses. As a result, personal information is kept private while allowing for the use of the data for study or analysis. Pseudonymized data can still be used to relate to the original data collection, but doing so requires further details, like a key or token.
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Anonymization
Data is anonymized during the anonymization process, making it difficult to link it to specific individuals. All data that could be used to identify specific people, such as names, addresses, and other personal information, are normally removed or obscured to do this. It is impossible to re-identify someone using anonymized data because it cannot be linked to any other data set.
When created with the proper privacy safeguards, synthetic data can provide a defense against potential adversarial assaults that conventional anonymization methods like masking or tokenization cannot.
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Recital 26 of GDPR
As a result, according to Recital 26 of the GDPR, the principles of data protection should not be applied to anonymous information, which is data that doesn’t relate to an identified or identifiable natural person, or to personal data that has been made anonymous in a way that makes it impossible to identify the data subject.
It continues by stating that pseudonymous data, which is information that has had personal identifying information replaced with a pseudonym, can still be regarded as personal data if the data controller or processor can connect the pseudonym to an identified or identifiable natural person using additional information they already have or can easily access.
This indicates that if synthetic data is created appropriately and with the proper filters, it will fall beyond the purview of GDPR and eliminate all the dangers associated with handling and transmitting actual data that contains PII.
Tools and Stack
A wide range of businesses employing synthetic data to solve various problems and open up new opportunities in a variety of industries make up the startup ecosystem of companies working on synthetic data difficulties. These companies often focus on using synthetic data to promote fairness, privacy, and efficiency of data-driven processes, as well as the effectiveness of artificial intelligence and machine learning systems.
Synthetic vs. Real Data
It can be risky to gather real data. AI for driverless vehicles, for instance, cannot solely rely on real-world data. Companies developing this technology must run simulations. You need training data on collisions to teach an AI to avoid an automobile accident. However, gathering vast datasets of real motor accidents is simply too risky and expensive, so you simulate crashes instead.
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Real Data can be hard to come by
Data that can only be collected very seldom can likewise be subject to the principle of risky collection. Synthetic data can produce unusual events in sufficient quantities to precisely train a model, for example, if your AI system is searching for a “needle in a haystack.”
Take into account that some of the most advantageous applications of AI are centered on “rare” events. Rare events are difficult to record because of the nature of these issues. As we mentioned earlier, automotive accidents don’t happen very often, thus you virtually never get the opportunity to obtain this information. You can decide how many crashes you wish to mimic using synthetic data.
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User Control over Synthetic Data is Complete
A synthetic data simulation allows for complete control over every aspect. A blessing and a curse, both. It can be a curse since sometimes synthetic data miss edge situations that can be observed in real datasets.
You might use transfer learning for these applications to blend some real data with your synthetic datasets. The fact that you may select the event frequency, item distribution, and other factors makes this an advantage as well.
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Synthetic Data is Perfectly Annotated
The flawless annotation offered by synthetic data is another benefit. There will never be a need for manual data collection again. A scene’s objects can all automatically produce several types of annotations. Even though it may not seem significant, this is one of the primary causes for why synthetic data is so much less expensive than real data.
The labeling of data is free. Instead, the initial expenditure in creating the simulation is the primary cost of using synthetic data. The cost-effectiveness of creating data over genuine data increases rapidly after that.
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Synthetic Data can be Multispectral
Companies that manufacture autonomous vehicles have realized how difficult it is to annotate non-visible data. They have therefore been among the strongest supporters of synthetic data. Because these data are synthetic and the labels are automatically placed, the actual situation is understood. Synthetic data works effectively for computer vision applications that use infrared or radar imaging when humans can’t fully interpret the imagery.
Where can you Apply Synthetic Data?
Tabular data and computer vision are currently the two main areas of synthetic data. When an AI algorithm is used to find patterns and objects in photos, this is known as computer vision. From drones to medicine, from the auto industry to many other sectors, cameras are being used more and more.
The development of computer vision technology is still in its infancy due to the combination of synthetic data and more sophisticated AI. Another use for synthetic data is tabular data. Researchers pay a lot of attention to tabular synthetic data.
Particularly suitable for a synthetic approach are data on privacy and health. Significant limits are imposed on these fields by privacy laws. Researchers can obtain the data they require via synthetic data without invading people’s privacy. Artificial intelligence (AI) will be able to use synthetic data more and more when new tools and courses are made available.
Synthetic Data FAQ’s
Synthetic data can be used to enhance fairness, bias, and the resilience of machine learning systems, but much more research is required to fully understand the benefits and limitations of this approach.
It is created using computer algorithms or simulations. Synthetic data generation is generally utilized when actual data is either unavailable or needs to remain secret owing to issues with personally identifiable information (PII) or compliance.
Information that has been artificially annotated and is generated by computer simulations or algorithms is referred to as synthetic data. When sufficient real-world data is not available, synthetic data is frequently used as a substitute, for example, to add more examples to a small machine-learning dataset.
Models trained using synthetic data can sometimes beat other models in terms of accuracy, which may relieve some of the ethical, copyright, and privacy concerns related to using real data.
Machine learning models can be trained using synthetic data instead of real-world data. Machine learning models can be trained on a broader and more varied set of data by using synthetic data to complement or replace real-world data.
The difficulty of verifying the accuracy of synthetic data is another drawback. It can be challenging to determine whether a synthetic dataset effectively replicates the underlying trends of real-world data, even though it could appear realistic and precise.
Because synthetic data only approximates real-world data and is not a replica of it, outliers are difficult to map. As a result, the synthetic data might not completely account for some outliers in the actual data.
It is vital to control the random processes that produce data based on statistical distributions or generative models to make sure the product is sufficiently diverse while remaining realistic. Customizable synthetic data is necessary.
The original image can be transformed using augmentation techniques, whereas a synthetic image synthesis methodology allows us to alter the distribution to produce new data.
The use of more synthetic examples of minority classes than what is available and simply giving more data for training are two ways that synthetic data improve machine learning performance. The performance of machine learning models can increase by up to 15% depending on the precise dataset and model.
Second, it’s not always okay to share data. There may be delicate personally identifiable information (PII) present. Sharing the data with new teams may be beneficial to speed up their exploration and analysis work, but doing so may involve time-consuming redaction, special handling, form completion, and other administrative tasks.
By releasing information that resembles sensitive data but isn’t actual data, synthetic data provides a medium ground. Even this, in some circumstances, might be problematic because what if the fake data occasionally looks too much like the real thing? Other times, it might not be enough.
