The intersection of data privacy and artificial intelligence (AI) presents a paradox as the world has changed dramatically. To perfectly find a balance between the two sides, synthetic data generation has appeared as a good solution both respecting privacy and fueling AI innovation.
In data science and AI, synthetic data refers to datasets generated by algorithms mimicking real data.
Training AI models, as we all know, requires a large amount of multimodal datasets. Compared to synthetic datasets, real-world datasets are derived from actual events, as they can enhance AI's reliability and relevance.
Synthetic data, to some extent, can be described as "fake data." Sophisticated algorithms can model the underlying structure of real data. These algorithms can understand and replicate the distribution, correlations, and other features of the data they are mimicking.
At present, synthetic data offers an opportunity to create and examine AI models and systems without putting people's private data at risk. This is important in healthcare, finance, or marketing where rules about keeping data private are strict, and breaking them can have serious outcomes.
Fully Synthetic Data: It does not have any link to real data. Its generation is wholly from statistical models or algorithms, and all variables are made without identifiable information.
Partially Synthetic Data: Partially synthetic data keeps a few details from the original data but changes sensitive parts to protect privacy. It finds a middle ground between real data and privacy by replacing or modifying certain fields that might uncover personal information.
Synthetic Time Series Data: This refers to a certain type of synthetic data that simulates a series of data points in time. This can be used to model sequences like stock prices, weather patterns or sensor readings.
Synthetic Text Data: For natural language processing (NLP) tasks, synthetic text data imitates human language, consisting of sentences, paragraphs, or documents.
Synthetic Image Data: It is like photographic pictures, videos, or 3D models that mimic real-life imagery. This type of data gets created for computer vision datasets and applications.
Synthetic Audio Data: This data is made to imitate genuine sounds from the real world or even speech. Sound datasets and speech datasets can train AI systems to recognize speech or handle different audio analysis jobs without needing recordings of real voices.
Synthetic Tabular Data: Tabular synthetic data is organized in rows and columns like what you see in spreadsheets or relational databases. It can be utilized for regression, classification, or clustering.
Synthetic Sensor Data: Made to look like data from different types of sensors such as GPS, accelerometers or environmental sensors. This type of synthetic data is useful for testing and training AI systems that rely on sensor inputs.
Synthetic Combination Data: Making multimodal synthetic data. For example, text with pictures or sensor details to replicate complicated scenarios having many modes.
Step 1 Data understanding
Start by fully understanding the real dataset, such as distribution, relationships between variables, absent data elements and extreme values.
Step 2 Data preprocessing
Remove or impute missing values, correct errors, and standardize the format of the data.
Also, remove or encrypt any personally identifiable information (PII) to ensure privacy.
Step 3 Feature selection
Determine which features contain sensitive information that needs to be masked or altered.
Choose the features that you can use for synthetic data generation, or change a little bit.
Step 4 Statistical modeling
Use statistical methods to model the distribution of each feature in the dataset.
Analyze relationships between features (e.g., correlations, causations) and model them accordingly.
Step 5 Choosing the synthetic data generation technique
Sampling: Generate synthetic data by drawing samples from the modeled distributions.
Machine Learning (ML) Models
Decision Trees: Used to model and generate synthetic data for classification or regression tasks.
Deep Learning: Employ more intricate models like Generative Pre-trained Transformers (GPT), Generative Adversarial Networks (GANs), or Variational Autoencoders (VAEs) to generate complex synthetic datasets.
Step 6 Synthetic data creation
Create new data points by sampling from the modeled distributions and relationships.
Make certain that the statistical properties of the original data are preserved by the synthetic data.
Step 7 Post-processing
Change synthetic data by adding small differences and avoiding consistent patterns implying artificially created.
Check the synthetic data to confirm that it does not carry any re-identifiable information of individuals.
Step 8 Validation and utility assessment
Verify synthetic data with the real data to guarantee it preserves matching statistical elements.
Assess the synthetic data's usefulness in its specific purpose, like testing machine learning models or data analysis.
Step 9 Iterative refinement
The validation process helps in enhancing the generation model by using feedback to create better synthetic data.
Step 10 Documentation and Transparency
Record how the fake data was generated, saving details about the models and techniques applied.
Make clear the application of synthetic data, particularly when sharing with third parties or using it in applications that impact end-users.
Datomize
Functionality: Datomize specializes in creating synthetic data twins that are statistically identical to real data, allowing for analytics to maintain privacy.
Applications: Finance, healthcare, and any other field requiring high-fidelity synthetic data for analysis or ML.
Synthesized
Functionality: Provides a comprehensive platform for generating synthetic data, including data augmentation, collaboration, and secure sharing.
Applications: This platform is useful in many areas, including e-commerce, finance, and healthcare, for creating varied and representative datasets.
MOSTLY.AI
Functionality: Focuses on privacy-first synthetic data generation, it extracts patterns from real data to create fresh datasets without showing confidential information.
Applications: Particularly beneficial for industries like banking and insurance.
Hazy
Functionality: Hazy generates synthetic data for training ML models in the finance industry without using real customer data.
Applications: Fintech organizations and banks can incorporate Hazy into their analytics processes, preventing fraud and preserving privacy.
Sogeti
Functionality: Provides a cognitive-based solution with Artificial Data Amplifier technology for data synthesis and processing.
Applications: It can be used in various industries such as healthcare or manufacturing because they need this for making predictions by combining complex data.
Rendered.AI
Functionality: Generates physics-based synthetic datasets for industries like satellite imaging, robotics, and autonomous vehicles.
Applications: Ideal for engineers and data scientists working on high-stakes projects that require accurate and varied datasets.
Scikit-learn
Functionality: A popular ML library in Python that also offers tools for generating simple synthetic data.
Applications: Good for learning, prototyping, or generating basic datasets for regression, classification, and clustering tasks.
Numpy/Pandas
Functionality: These two are also Python libraries. They can be used to generate and manipulate synthetic numerical and tabular data.
Applications: Scientific computing, data analysis, and for creating datasets for statistical modeling.
Pydbgen
Functionality: Allows for generating categorical data such as random names, phone numbers, and email addresses.
Applications: Synthetic datasets in social sciences, customer analytics, and any field where categorical data is needed.
GAN Dissection
Functionality: A GAN model that generates images and allows users to understand and manage the generation procedure.
Applications: Suitable for researchers and developers working on computer vision tasks who need precise control over the generation of synthetic images.
TensorFlow Datasets
Functionality: It comes with a collection of datasets ready to use with TensorFlow, including some synthetic datasets to train ML models.
Applications: Developers and researchers use TensorFlow to require access to varied and prepared datasets.
Compared to real-world data, synthetic data is also helpful in machine learning.
1. Privacy Preservation: Synthetic data does not include personal information, so it can be used for AI model training that keeps privacy intact.
2. Data Scarcity: Some domains contain scarce real data. Synthetic data can provide the necessary volume to train complex models.
3. Bias Mitigation: Synthetic data, when generated with careful design, can assist in lowering biases within real-world datasets and thus promote more equitable AI models.
4. Diversity and Inclusivity: Synthetic data generation has the potential to improve the diversity and inclusivity of AI models by including underrepresented groups or situations.
5. Cost: Synthetic data is less expensive to create when compared to gathering, cleaning, and labeling real-world data.
6. Experimentation: Synthetic data allows for more experimentation and quick prototyping without the risk of utilizing real data.
7. Regulatory Compliance: Synthetic data assists organizations in meeting the requirements of data protection regulations like GDPR, as it does not use sensitive personal data.
8. Exploratory Analysis: Synthetic data could be employed for investigating possible situations and unique cases that might not exist in actual data from the world.
The path forward is always with challenges. So it does for synthetic data generation.
One of the main problems is making synthetic data that is statistically comparable, as well as contextually and semantically similar. It becomes important in tasks where even nuances in data may influence the model's performance significantly.
Also, generating data that allows machine learning models to generalize well across various real-life scenarios is challenging. However, it may be difficult to ensure synthetic data does not possess or generate biases.
In the long term, finding a balance in hybrid models between synthetic and real data might be challenging as well. It may require more sophisticated approaches to merge and make use of the advantages of both types effectively.
The use of synthetic data will increase due to the complexity and demand for more training data in AI models. Synthetic data aids in creating diverse and large datasets that enhance the model's robustness and efficiency.
In some areas, it might be difficult to locate and assemble authentic data from the world. Synthetic data can help fill the gap when real information is not available. This approach could offer a more even combination of both natural and fabricated datasets in certain situations.
On the whole, as technology advances, there could be a rapid increase in synthetic data. This might lead to faster AI innovation where there is no need to wait for real-world data collection.
