Chatting with ChatGPT about synthetic data

Real interview, AI chatbot: We gave ChatGPT the FAQs of synthetic data

Generative AI has continuously been grabbing the tech headlines through 2022. In particular OpenAI has led the pack with the release of Dall-E 2, which creates and edits images from text using CLIP (Contrastive Language–Image Pre-training), as well as ChatGPT, a conversation AI system relying on GPT-3.5 (Generative Pre-trained Transformer 3.5).

We decided to have some pre-end of year fun and probe these two technologies by answering some of the frequently asked questions about synthetic data and privacy. The above image was generated by Dall-E 2 with the prompt “privacy-preserving synthetic data created by generative machine learning models satisfying differential privacy”.

Please be advised that we have included only answers that we believe are scientifically correct since large language models (LLMs), and in particular ChatGPT, are not completely reliable, could generate wrong information, and currently do not cite their sources. Let’s jump into it.

• Q1. What is synthetic data?
• Q2. How do you generate synthetic data?
• Q3. What are some good use cases for synthetic data?
• Q4: Is synthetic data trained on sensitive data automatically private?
• Q5: What is privacy-preserving synthetic data?
• Q6: Why is differential privacy important for synthetic data generation?
• Q7: Can you explain differential privacy to me as if I’m 6?
• Q8: What about regulation?
• Q9: Is synthetic data GDPR compliant?
• Q10: How would you convince an enterprise that synthetic data is safe to use?
• Q11: How do you generate synthetic data?
• Q12: How do you apply differential privacy in the context of synthetic data?
• Q13: Can you guarantee the privacy in synthetic data through empirical evaluation?
• Q14: Which is better to use in order to get synthetic data with higher utility: generative machine learning models or hand engineered methods?
• Q15: List 5 models for synthetic data generation?

Q1. What is synthetic data?

Synthetic data is data that is generated by a computer program or algorithm, rather than being collected from real-world sources. It is often used in machine learning and other fields to train models and algorithms, since it can be more readily available and easier to work with than real-world data. Synthetic data can also be used to test and evaluate models, since it can be created with known properties and can be used to create more consistent and controlled test conditions.

Q2. How do you generate synthetic data?

To generate synthetic data, one can use a variety of methods, such as generative machine learning models, simulation based on known statistical properties or trends, or data generation tools and software. To generate synthetic data using a generative machine learning model, the model is first trained on a dataset by feeding it a large amount of data and using a machine learning algorithm to train the model to generate data that is similar to the data in the original dataset. Once the model has been trained, it can be used to generate synthetic data by providing it with input parameters and using the training it received to generate new data that is similar to the data in the original dataset. This generated data can then be used for a variety of purposes.

Q3. What are some good use cases for synthetic data?

There are many potential use cases for synthetic data, including the following:

• Research: Synthetic data can be used in research to study a wide range of phenomena, such as public health trends or the impact of different policies, without exposing sensitive personal information.
• Testing: Synthetic data can be used to test machine learning models or other software systems, allowing developers to evaluate their performance without having access to real data.
• Privacy: Synthetic data can be used as an alternative to real data when privacy is a concern, such as in situations where data cannot be shared or released due to legal or ethical considerations.
• Data Augmentation: Synthetic data can be used to augment existing datasets, helping to improve the performance of machine learning models or to train models on a larger or more diverse dataset.
• Data Generation: Synthetic data can be generated on demand, allowing organizations to create data for specific purposes, such as training machine learning models or conducting research.

Overall, synthetic data has the potential to provide many benefits, including improved privacy, more efficient and effective research, and greater flexibility in the use of data.

Q4: Is synthetic data trained on sensitive data automatically private?

Synthetic data trained on sensitive data is not automatically private, and using sensitive data to train a generative model can raise serious privacy concerns. Sensitive data is data that contains personally identifiable information or other sensitive information that could be used to harm individuals if it were to be exposed. If a generative model is trained on sensitive data, the synthetic data it generates will be based on that sensitive data, which means that it could potentially contain sensitive information.

Additionally, even if the synthetic data generated by a model trained on sensitive data does not contain any personally identifiable information, it could still be used to train other machine learning models that could potentially be used to infer sensitive information or make decisions that have negative consequences for individuals. As such, it is important to carefully consider the potential privacy implications of using sensitive data to train generative models, and to take appropriate steps to protect the privacy of individuals whose data is used in this way. In general, it is always important to handle sensitive data carefully and to follow best practices for protecting privacy when working with this type of data.

Q5: What is privacy-preserving synthetic data?

Privacy-preserving synthetic data is data that has been generated by a machine learning model in order to preserve the statistical properties of the original data, while also protecting the privacy of the individuals whose data was used to train the model. This is typically done by using a technique called differential privacy, which involves adding random noise to the data in such a way that it is impossible to determine the exact values of the original data points. The resulting synthetic data can be used for research or other purposes, without compromising the privacy of the individuals whose data was used to create it.

Q6: Why is differential privacy important for synthetic data generation?

Differential privacy is important for synthetic data generation because it provides a formal definition of privacy and a set of tools and techniques for protecting privacy when generating synthetic data. By using differential privacy, it is possible to generate synthetic data that is useful and accurate, while also protecting the privacy of individuals whose data is included in the dataset. Differential privacy can be used to measure the privacy of synthetic data and to evaluate the trade-off between the utility of the data and the privacy of individuals, and can help ensure that the synthetic data does not expose any sensitive information or personally identifiable information. Overall, differential privacy is an essential concept for synthetic data generation, as it helps protect the privacy of individuals while enabling the use of synthetic data for training and testing machine learning models.

Q7: Can you explain differential privacy to me as if I’m 6?

Certainly! Differential privacy is a way of keeping your information private when it is used by computers to learn and make decisions. Imagine that you have a special box that you use to keep your secrets. Differential privacy is like a rule that says that nobody can open the box to see your secrets, but they can still learn from the box and make good guesses about what might be inside. This way, your secrets are kept safe and private, but the computer can still use the information in the box to learn and make good decisions.

Differential privacy is used in many different ways, but one common example is when a computer is trying to learn from a large amount of data. The computer might use differential privacy to protect the information in the data, so that it can't be used to identify or harm anyone. This way, the computer can learn from the data and make good decisions, but the information in the data is kept private and protected. Differential privacy is an important concept that helps to protect your information and keep it private when it is used by computers.

Q8: What about regulation?

Regulation can play a role in ensuring the safety and privacy of synthetic data. For example, in the European Union, the General Data Protection Regulation (GDPR) sets out specific rules and requirements for the use of personal data, including synthetic data. These rules apply to any organization that processes personal data, including the generation and use of synthetic data, and they require organizations to take appropriate measures to protect the privacy of individuals. In the United States, there is no comprehensive federal law that specifically addresses the use of synthetic data, but there are a number of sector-specific laws, such as the Health Insurance Portability and Accountability Act (HIPAA), that may apply to the use of synthetic data in certain contexts.

Overall, it is important for organizations that use synthetic data to be aware of the relevant regulations and to take steps to ensure that they are compliant. This may include implementing appropriate privacy safeguards, such as using differential privacy, and being transparent about how the synthetic data is being used.

Q9: Is synthetic data GDPR compliant?

Synthetic data can be GDPR compliant if it is generated and used in a way that is consistent with the requirements of the GDPR. The GDPR is a comprehensive data protection law that applies to any organization that processes personal data, including the generation and use of synthetic data. The GDPR sets out specific rules for the use of personal data, including requirements for ensuring the privacy and security of data, and it gives individuals certain rights with respect to their personal data.

In order to be GDPR compliant, synthetic data must be generated in a way that protects the privacy of the individuals whose data was used to train the model. This can be achieved through the use of techniques such as differential privacy, which adds random noise to the data in such a way that it is impossible to determine the exact values of the original data points. In addition, organizations that use synthetic data must ensure that they are transparent about how the data is being used and provide individuals with information about their rights under the GDPR.

Overall, synthetic data can be GDPR compliant if it is generated and used in a way that is consistent with the requirements of the GDPR.

Q10: How would you convince an enterprise that synthetic data is safe to use?

There are several key points that can be used to convince an enterprise that synthetic data is safe to use. These include the following:

• Synthetic data is generated by a machine learning model, which means that it preserves the statistical properties of the original data while protecting the privacy of the individuals whose data was used to train the model.
• Synthetic data is often generated using techniques such as differential privacy, which add random noise to the data in such a way that it is impossible to determine the exact values of the original data points. This makes it highly unlikely that anyone could use the synthetic data to identify or re-identify individual data subjects.
• Synthetic data can be used for many of the same purposes as real data, including research, testing, and training machine learning models, without exposing sensitive personal information.
• Synthetic data can provide many benefits, such as improved privacy, more efficient and effective research, and greater flexibility in the use of data.

Overall, synthetic data can be a safe and valuable tool for enterprises, providing a way to use data for important purposes while also protecting the privacy of individuals.

Q11: How do you generate synthetic data?

There are many different ways to generate synthetic data, depending on the specific application and the type of data being generated. Some common methods for generating synthetic data include using generative machine learning models, such as generative adversarial networks (GANs) or variational autoencoders (VAEs), simulating data based on known statistical properties or trends, and using data generation tools or software.

To generate synthetic data using a generative machine learning model, the first step is to train the model on a dataset. This involves feeding the model a large amount of data, and using a machine learning algorithm to train the model to generate data that is similar to the data in the original dataset. Once the model has been trained, it can be used to generate synthetic data that is similar to the data used to train the model.

To simulate data based on known statistical properties or trends, the first step is to determine the statistical properties or trends that are relevant to the data being generated. This might involve analyzing real-world data to identify common patterns or trends, or using domain knowledge to determine the likely distribution of the data. Once the relevant statistical properties or trends have been identified, a computer program or algorithm can be used to generate data that follows those properties or trends.

Finally, data generation tools and software can be used to generate synthetic data more quickly and easily. These tools often provide a user-friendly interface for generating data, and may include pre-defined templates or settings for common data types or scenarios. Some data generation tools may also be integrated with other software, such as machine learning frameworks, to make it easier to use the generated data in other applications.

Q12: How do you apply differential privacy in the context of synthetic data?

In the context of synthetic data, differential privacy can be applied by using a differentially private algorithm to generate the synthetic data. This involves adding carefully calibrated noise to the data in a way that ensures that sensitive information or personally identifiable information is not exposed when using the synthetic data for training or testing machine learning models.

One common approach for applying differential privacy to synthetic data is to use a differentially private generative model, such as a Generative Adversarial Network (GAN) or a Variational Autoencoder (VAE). These models can be trained using differentially private algorithms, which add noise to the data in a way that ensures that the synthetic data generated by the model does not contain any sensitive information or personally identifiable information.

Q13: Can you guarantee the privacy in synthetic data through empirical evaluation?

It is not possible to guarantee the privacy of synthetic data through empirical evaluation alone. Empirical evaluation is a method of testing or evaluating a system or model by collecting and analyzing data from real-world observations or experiments. While empirical evaluation can provide useful insights and information about the performance of a system or model, it cannot guarantee the privacy of synthetic data.

The privacy of synthetic data depends on the specific methods and algorithms used to generate the data, and on the potential risks to privacy that are present in the dataset. To guarantee the privacy of synthetic data, it is necessary to carefully assess these risks and to take appropriate steps to mitigate them, using a combination of techniques and best practices as appropriate.

For example, to guarantee the privacy of synthetic data, it may be necessary to use data anonymization and de-identification techniques, access controls, and privacy-enhancing technologies. Additionally, it may be necessary to use formal definitions of privacy, such as differential privacy, to ensure that the synthetic data does not expose any sensitive information or personally identifiable information.

Overall, while empirical evaluation can provide valuable insights into the performance of synthetic data, it cannot guarantee the privacy of the data. To guarantee the privacy of synthetic data, it is necessary to carefully assess the potential risks to privacy and to take appropriate steps to mitigate those risks.

Q14: Which is better to use in order to get synthetic data with higher utility: generative machine learning models or hand engineered methods?

It depends on the specific use case. In general, generative machine learning models can produce highly realistic synthetic data that can be useful for a wide range of applications. These models are typically trained on large amounts of real-world data, which allows them to learn the underlying patterns and characteristics of the data. This makes them well-suited for generating synthetic data that has similar properties to the real-world data it was trained on.

Q15: List 5 models for synthetic data generation?

There are many different models that can be used for synthetic data generation, including:

• Generative adversarial networks (GANs): These are a type of deep learning model that consist of two networks, a generator and a discriminator, that work together to generate synthetic data.
• Variational autoencoders (VAEs): These are a type of deep learning model that can be used to generate synthetic data by learning a continuous latent representation of the data.
• Autoregressive models: These are a type of statistical model that can be used to generate synthetic time-series data by modeling the dependencies between successive data points.
• Markov chain Monte Carlo (MCMC) methods: These are a class of computational algorithms that can be used to generate synthetic data by sampling from a complex probability distribution.
• Bayesian network models: These are a type of probabilistic graphical model that can be used to generate synthetic data by representing the relationships between variables as a network of nodes and edges.