Synthetic Data: Enhancing Data Security with Betterdata

Investors in Singapore-based startup Betterdata are betting that a smarter way to protect data will spark wider adoption of the technology. The company’s programmable synthetic data, which is created and stored on a blockchain-based platform, is seen as a potential solution to the ever-growing danger of cybercrime.

TrafficData is a blockchain-based startup that aims to make data sharing faster and more secure by building a platform that allows users to share data securely and quickly. With strict new data protection regulations hitting the world, TrafficData believes its platform can be a valuable tool for organizations who need to protect their sensitive information.

Traditional methods of data sharing use anonymization to destroy data. However, Betterdata uses generative AI and privacy engineering in order to create data that is still useful but keeps the individual’s identity anonymous. This method allows for more accurate analyses and insights to be generated while also protecting the privacy of the individual.

Programmatically synthesizing data is a process of creating new datasets based on previously unused information. This can be done using generative models, which are deep learning models that include generative adversarial models (DAMs), transformers, and diffusion models. This can create datasets that are fake but also more realistic than those created through traditional methods.

The datasets presented here are synthetic data created using a methodology similar to that used by real-world data providers. The datasets are structurally and qualitatively similar to real-world data, except that they do not include any sensitive or private information about individuals.

If the team’s algorithm can effectively create a fictional dataset that is representative of the original, it could help safeguard confidential data, reduce bias in machine learning models and improve their accuracy.

Overall, programmatic synthetic data can be a helpful asset for developers in many ways. For example, it can help them protect sensitive data, comply with data protection regulations like GDPR and HIPAA, increase data availability between teams, create more data to train and test machine learning models, and address data imbalance issues.

Betterdata’s synthetic data technology takes tabular datasets and turns them into a more valuable asset for businesses. The company plans to use its funding to launch its product and improve its programming tools so that businesses can work with synthetic data more easily.

Different types of datasets can be very useful for specific purposes. For example, a single-table dataset is great for studying individual tables without considering any relationships between them, while a multi-table dataset can be helpful in understanding how different tables are related. Lastly, time-series datasets can be incredibly useful when tracking changes over time.

Betterdata is looking to grow its operations in order to better serve its customers. In addition to hiring additional employees, the company plans to expand its presence into more of the Asia-Pacific region over the next one to two years. Betterdata believes that by expanding its reach, it can provide even more valuable insights and services to customers beyond Singapore.

Khairu Rejal’s statement about Betterdata underscores the importance of data quality and privacy compliance in the AI industry. The synthetic data produced by Betterdata meets both requirements, making it a valuable tool for businesses wishing to expand globally while keeping their customers’ information protected.

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Max Chen

Max Chen is an AI expert and journalist with a focus on the ethical and societal implications of emerging technologies. He has a background in computer science and is known for his clear and concise writing on complex technical topics. He has also written extensively on the potential risks and benefits of AI, and is a frequent speaker on the subject at industry conferences and events.

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