Privacy in Training Data: Randomization techniques
1. Privacy in Training Data: Randomization techniques
Let's move to randomization techniques for privacy training in data. Randomization techniques aim to preserve data privacy by adding noise or perturbation to the data. When preserving data privacy, you can apply techniques like data perturbation or differential privacy. Data perturbation is a technique you can implement easily. Whereas differential privacy provides a more rigorous privacy guarantee. Let's explore each of these techniques by looking at each method. Data perturbation introduces some random noise or makes small modifications to obflicate as sensitive value. The perturbed data still reflects the general trends and patterns present in the original data set while preventing the direct identification of individuals. Some examples of data perturbation techniques include random noise addition, adding random values from a small range to each data point in the dataset. This works for numerical values. Random swap, exchanging the values of different data points randomly. It works for both numerical and categorical values. Random rounding, rounding data points to random precision levels. This works for numerical values. Random category mapping, randomly mapping categorical values to different categories within the same variable. The level of perturbation must strike a balance between privacy protection and data utility, ensuring that the data remains informative and valuable for legitimate analytical purposes. Differential privacy ensures that the inclusion or exclusion of any individual's data does not significantly affect the output or result of the analysis. This is particularly useful when you want to share data or allow inferences about groups of people while preventing someone from learning information about an individual. It is referred to as a rigorous approach since it provides strong provable privacy guarantees. Let's take a look at an example of applying differential privacy. Imagine you are attending a conference and the moderator shares some insights about the audience. The moderator shows the country of origin of the participants in the room for day 1. The data is aggregated, so it does not reveal information about any of the individuals in the room. The data reveals the top country of origin is Italy, whereas Germany has the least number of participants, and France and Spain participant numbers are in the middle. On day 2, the moderator shows the stats for the day. It looks very similar to the chart from day 1. However, before the day 2 session started, you met someone who attended day two, but not day 1. Knowing this information, you compare the data from both days together. Are you able to determine the country of origin for the new attendee based on the datasets? Yes, the New attendee is from Germany. This is an example of how given two aggregated datasets. The moderator has not protected the privacy of particular participant. To address the data privacy leak issue, the moderator needs a new approach. Let's try to randomly change the numbers a little to improve data privacy. Now, look at how the day one stats look compared to the real numbers. They're slightly different. It is less precise, but you still get the same insights about the statistical trends. The same approach is applied to the data from day 2. Now, if you compare the stats for day 1 and day 2, you can't tell which country the single new attendee is coming from, and it's not worth trying to figure out since you know the numbers were changed a little. This is the principle behind differential privacy. Differential privacy introduces the right amount of noise to the data. We're able to provide useful statistically significant insights while ensuring that no one can tell if a particular individual's information was used in the computation. The best thing about this principle is that it is mathematically proven. Differential privacy relies on this equation. It essentially says that something is differentially private. If the output doesn't change much when a single person is added to the database. Going back to the previous example, a differentially private version of those charts doesn't allow us to tell much about the participants when a new one is added. Differential privacy is characterized by two key parameters. The privacy parameter Epsilon is a non negative value that quantifies the privacy level provided by the differential privacy mechanism. A smaller Epsilon value indicates stronger privacy protection, but it comes with the risk of too much noise being added to the query results, potentially reducing data utility. Sensitivity measures how much the output of a query can change, specifically the maximum absolute difference when a single individual's data is added or removed from the dataset. Sensitivity is crucial for determining the amount of noise that needs to be added to ensure privacy. Techniques in de identification and randomization are used to protect sensitive data. These techniques will help improve privacy in your training data for AI models.2. Let's practice!
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