Synthetic data: the key to training AI without compromising privacy or compliance

by Huenei IT Services | Oct 1, 2024 | Data

Training AI Safely with Synthetic Data

Training artificial intelligence (AI) models requires vast amounts of data to achieve accurate results. However, using real data poses significant risks to privacy and regulatory compliance. To address these challenges, synthetic data has emerged as a viable alternative.

These are artificially generated datasets that mimic the statistical characteristics of real data, allowing organizations to train their AI models without compromising individual privacy or violating regulations.

Regulatory Compliance, Privacy, and Data Scarcity

Regulations around the use of personal data have become increasingly strict, with laws such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States.

This approach to data provides a solution for training AI models without putting personal information at risk, as it does not contain identifiable data, yet remains representative enough to ensure accurate outcomes.

Use Cases for Synthetic Data

The impact of this technology extends across multiple industries where privacy protection and a lack of real-world data present common challenges. Here’s how this technology is transforming key sectors:

Financial

In the financial sector, the ability to generate artificial datasets allows institutions to improve fraud detection and combat illicit activities. By generating fictitious transactions that mirror real ones, AI models can be trained to identify suspicious patterns without sharing sensitive customer data, ensuring compliance with strict privacy regulations.

For instance, JPMorgan Chase employs synthetic data to bypass internal data-sharing restrictions. This enables the bank to train AI models more efficiently while maintaining customer privacy and complying with financial regulations.

Healthcare

In the healthcare sector, this approach is crucial for medical research and the training of predictive models. By generating simulated patient data, researchers can develop algorithms to predict diagnoses or treatments without compromising individuals’ privacy. Synthetic data replicates the necessary characteristics for medical analyses without the risk of privacy breaches.

For instance, tools like Synthea have generated realistic synthetic clinical data, such as SyntheticMass, which contains information on one million fictional residents of Massachusetts, replicating real disease rates and medical visits.

Automotive

Synthetic data is playing a crucial role in the development of autonomous vehicles by creating virtual driving environments. These datasets allow AI models to be trained in scenarios that would be difficult or dangerous to replicate in the real world, such as extreme weather conditions or unexpected pedestrian behavior.

A leading example is Waymo, which uses this method to simulate complex traffic scenarios. This allows them to test and train their autonomous systems safely and efficiently, reducing the need for costly and time-consuming physical trials.

Generating and Using Synthetic Data

The generation of synthetic data relies on advanced techniques such as generative adversarial networks (GANs), machine learning algorithms, and computer simulations. These methods allow organizations to create datasets that mirror real-world scenarios while preserving privacy and reducing the dependence on sensitive or scarce data sources.

Synthetic data can also be scaled efficiently to meet the needs of large AI models, enabling quick and cost-effective data generation for diverse use cases.

For example, platforms like NVIDIA DRIVE Sim utilize these techniques to create detailed virtual environments for autonomous vehicle training. By simulating everything from adverse weather conditions to complex urban traffic scenarios, NVIDIA enables the development and optimization of AI technologies without relying on costly physical testing.

Challenges and Limitations of Synthetic Data

One of the main challenges is ensuring that synthetic data accurately represents the characteristics of real-world data. If the data is not sufficiently representative, the trained models may fail when applied to real-world scenarios. Moreover, biases present in the original data can be replicated in synthetic data, affecting the accuracy of automated decisions.

Constant monitoring is required to detect and correct these biases. While useful in controlled environments, synthetic data may not always capture the full complexity of the real world, limiting its effectiveness in dynamic or complex situations.

For organizations in these sectors, partnering with a specialized technology partner may be key to finding effective, tailored solutions.

The Growing Role of Synthetic Data

Synthetic data is just one of the tools available to protect privacy while training AI. Other approaches include data anonymization techniques, where personal details are removed without losing relevant information for analysis. Federated learning, which enables AI models to be trained using decentralized data without moving it to a central location, is also gaining traction.

The potential for synthetic data extends beyond training models. These data can be used to enhance software validation and testing, simulate markets and user behavior, or even develop explainable AI applications, where models can justify their decisions based on artificially generated scenarios.

As techniques for generating and managing synthetic data continue to evolve, this data will play an even more crucial role in the development of safer and more effective AI solutions.

The ability to train models without compromising privacy, along with new applications that leverage artificially generated data, will allow businesses to explore new opportunities without the risks associated with real-world data.

Are you ready to explore how we can help you safeguard privacy and optimize AI implementation in your organization? Let’s talk.

Get in Touch!
Isabel Rivas
Business Development Representative
irivas@huenei.com

Do you Know the Difference Between Data Engineering vs Data Science?

by Huenei IT Services | Feb 5, 2024 | Data

Do you Know the Difference Between Data Engineering vs Data Science?

Belonging to the world of technology involves hearing many concepts that may sound similar to each other. And one of them may be data engineering vs. data science. Although they share some similarities, the reality is that there are many important differences between them.

For this reason, the purpose of this article is to inform you and let you know what each concept means. Read on and find out more about the difference between data engineering vs data science!

Data engineering vs Data Science: what are the similarities and differences between the two terms?

Well, to learn more about data engineering vs. data science, it is necessary to know that in the world of technology and data there are many professions and roles. Precisely, this is the main shared characteristic between both concepts: both the engineer and the data scientist are constantly working with large volumes of Big Data.

However, the difference is in the purpose. Engineers are in charge of extracting large volumes of information and organizing databases. On the other hand, data scientists perform visualization tasks, diagramming learning tasks, and patterns on the data previously extracted by engineers.

For this reason, the tools used by each tend to vary. In the case of data scientists, they usually use resources such as Deep Learning, Machine Learning, data processors (such as Spark), or programming tools such as R or Python. In this way, engineers use databases such as SQL and NoSQL, the Hadoop ecosystem, and tools such as Apache Airflow or Dagster. 

It should be made clear that both are indispensable professions for any company that wants to take advantage of technology. However, this serves only as an introduction to the subject. For this reason, we recommend that you read on to find out more about each of these fields of work.

What does data engineering consist of?

Let’s specify a little bit the roles that are practiced in data engineering. According to Coursera, it is the practice of designing and building systems that collect and store large volumes of data. Therefore, the engineer is the person who is responsible for building and maintaining data structures for use in multiple applications.

The ultimate goal of the data engineer is to make all this data accessible for the organization to consider in decision-making. In other words, the idea is that this data is transformed into useful information that executives can use to maximize profits and see growth in the company.

It is for this reason that a data engineer must have advanced knowledge of databases. Likewise, as there is a trend towards cloud programming, he or she needs to know all these systems. This professional must also be able to work in conjunction with different departments, to understand the organization’s objectives. 

So, it is key to understand that data engineers will not only need to be passionate about programming. They will also need to have communication skills, as they will be working in conjunction with other departments and professionals, as is the case with data scientists.

And what specifically is Data Science?

Now, you may want to know more details about data scientists, which is another of the most sought-after professions by companies in recent years. IBM considers that data science combines knowledge in mathematics, statistics, programming, and artificial intelligence, to make efficient decisions and improve the company’s strategic planning.  

It should be noted that Data Science is not synonymous with Artificial Intelligence. In reality, a data scientist uses Artificial Intelligence to extract useful information from unstructured data. AI is a series of algorithms that mimic human intelligence to read and understand data, but it is the scientist who makes the final decision. 

This situation means that the data scientist has to be a person with a strong sense of logic. Not only will they have to work by studying the behavior of the data, but they will have to understand what the company wants. For this reason, they must not only master statistical software and programming but also have a strong interest in market and company situations. 

Similarly, it should be considered that the data scientist will not only obtain data from a single source, as a traditional data analyst would do. Here they will seek to have a global perspective of the problem. Although they will bring their subjectivity to include their point of view in the decision-making process, the objective data will reinforce their arguments.

In short, you have seen that understanding the difference between data engineering vs data science is not complicated at all. Both professions are essential to working with Big Data since taking advantage of large volumes of information is key to achieving great results in a company. We hope this article has cleared up your doubts!