Data Privacy: Quizzes to Better Protect Your Data – 2

Data privacy is one of the most important factor in data analysis. If you fail to keep it, you will not only lose the trust of your customers, but you can also face serious legal consequences. Data leakage can lead to significant financial penalties under laws such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). Additionally, a bleach of data privacy can harm your company’s reputation, resulting in lost business and a decrease in consumer confidence.

To effectively address these risks, we prepared a series of quizzes to test your aptitude in protecting data privacy. Today, we’re going to explain the quizzes and help you get insights from them.

Data Privacy Questioned

Quiz 1. It’s not possible to extract training data out of AI models output because of all the complicated computations. True or False.

it is indeed possible to extract, or infer, the training data from AI model outputs. This is because the outputs of a well-trained model by definition contain enough information to make educated guesses about the input data or characteristics of the training dataset.

This kind of vulnerability highlights that AI models can unintentionally reveal information about their training data, contrary to the belief that all internal data and operations are completely obscured by complex computations.

To mitigate the risks of revealing sensitive information through AI models, several methods can be utilized:

• Differential Privacy
• Data Sanitization
• Regularization
• Synthetic Data

Quiz 2. Choose all parties that can infringe on privacy from the data entered as a prompt when using LLMs.

1. LLM provider 2. Internet Service Provider 3. Other users of the LLM

All these parties potentially have the means to access or intercept the data that you put into LLMs. You probably can easily guess how LLM providers can get a chance to obtain their input data as well as Internet Service Providers because they’re located in the middle of data processing path. But how about other users? You must keep it in mind that you share LLMs with them. It means that the exact model that has trained on your data serves others with knowledge from your data.

To prevent this risks, you may consider to adopt

• End-to-End Encryption
• Data Minimization
• Synthetic data