Home Community What Is Synthetic Data? Their Types, Use Cases, And Applications For Machine Learning And Privacy

What Is Synthetic Data? Their Types, Use Cases, And Applications For Machine Learning And Privacy

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What Is Synthetic Data? Their Types, Use Cases, And Applications For Machine Learning And Privacy

The sector of Data Science and Machine Learning is growing each day. As latest models and algorithms are being proposed with time, these latest algorithms and models need enormous data for training and testing. Deep Learning models are gaining a lot popularity nowadays, and people models are also data-hungry. Obtaining such an enormous amount of knowledge within the context of different problem statements is sort of a hideous, time-consuming, and expensive process. The info is gathered from real-life scenarios, which raises security liabilities and privacy concerns. Many of the data is private and guarded by privacy laws and regulations, which hinders the sharing and movement of knowledge between organizations or sometimes between different departments of a single organization—leading to delaying experiments and testing of products. So the query arises how can this issue be solved? How can the information be made more accessible and open without raising concerns about someone’s privacy?  

The answer to this problem is something referred to as

So, What’s Synthetic Data?

By definition, synthetic data is generated artificially or algorithmically and closely resembles actual data’s underlying structure and property. If the synthesized data is sweet, it’s indistinguishable from real data.

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How Many Different Kinds of Synthetic Data can there be?

The reply to this query could be very open-ended, as data can take many forms, but majorly now we have 

  1. Text data
  2. Audio or Visual data (for instance, Images, videos, and audio)
  3. Tabular data

Use cases of synthetic data for machine learning

We are going to only discuss the use cases of only three varieties of synthetic data, as mentioned above.

  • Use of synthetic text data for training NLP models

Synthetic data has applications in the sphere of natural language processing. For example, the Alexa AI team at Amazon uses synthetic data to complete the training set for his or her NLU system (natural language understanding). It provides them with a solid basis for training latest languages without existing or enough consumer interaction data.

  • Using synthetic data for training vision algorithms

   Let’s discuss a widespread use case here. Suppose we wish to develop an algorithm to detect or count the variety of faces in a picture. We will use a GAN or another generative network to generate realistic human faces, i.e., faces that don’t exist in the actual world, to coach the model. One other advantage is that we will generate as much data as we wish from these algorithms without breaching anyone’s privacy. But we cannot use real data because it accommodates some individuals’ faces, so some privacy policies restrict using that data.

One other use case is doing reinforcement learning in a simulated environment. Suppose we wish to check a robotic arm designed to grab an object and place it in a box. A reinforcement learning algorithm is designed for this purpose. We’d like to do experiments to check it because that is how the reinforcement learning algorithm learns. Establishing an experiment in a real-life scenario is sort of expensive and time-consuming, limiting the number of various experiments we will perform. But when we do the experiments within the simulated environment, then organising the experiment is comparatively inexpensive as it should not require a robotic arm prototype.

Tabular synthetic data is artificially generated data that mimics real-world data stored in tables. This data is structured in rows and columns. These tables can contain any data, like a music playlist. For every song, your music player maintains a bunch of knowledge: its name, the singer, its length, its genre, and so forth. It might even be a finance record like bank transactions, stock prices, etc.

Synthetic tabular data related to bank transactions are used to coach models and design algorithms to detect fraudulent transactions. Stock price data from the past could be used to coach and test models for predicting future prices of stocks.

One among the numerous benefits of using synthetic data in machine learning is that the developer has control over the information; he could make changes to the information as per the necessity to test any idea and experiment with that. Meanwhile, a developer can test the model on synthesized data, and it should give a really clear idea of how the model will perform on real-life data. If a developer desires to try a model and waits for real data, then acquiring data can take weeks and even months. Hence, delaying the event and innovation of technology.

Now we’re able to discuss how synthetic data help to resolve the problems related to data privacy.

Many industries depend upon the information generated by their customers for innovation and development, but that data accommodates Personally Identifiable Information (PII), and privacy laws strictly regulate the processing of such data. For example, the General Data Protection Regulation (GDPR) forbids uses that weren’t explicitly consented to when the organization collected the information.‍ As synthetic data very closely resemble the underlying structure of real data and, at the identical time, ensures that no individual present in the actual data could be re-identified from the synthetic data. Because of this, the processing and sharing of synthetic data have much fewer regulations, leading to faster developments and innovations and quick access to data.

Conclusion

Synthetic data has many significant benefits. It gives ML developers control over experiments and increases development speed as the information is now more accessible. It promotes collaboration on an even bigger scale since data is freely shareable. Moreover, synthetic data guarantees to guard the privacy of the individuals from the actual data.


Vineet

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Vineet Kumar is a consulting intern at MarktechPost. He’s currently pursuing his BS from the Indian Institute of Technology(IIT), Kanpur. He’s a Machine Learning enthusiast. He’s enthusiastic about research and the newest advancements in Deep Learning, Computer Vision, and related fields.


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