What is Differential Privacy: Definition, Mechanisms, and Examples

Differential privacy (DP) became an important component in privacy-preserving data analysis, especially when used intentionally in a broader context where other technologies are available.

It provides data professionals with a controlled way to prevent individual records from being identified by adding noise to data.

While offering significant privacy assurance, DP is most effective as part of a broader data security platform rather than as a standalone solution. For example, Anonos Data Embassy integrates various protection techniques, including DP, providing not only solid data protection but perseverance of data utility.

In this article, you’ll read about the origins and developments of DP, discover its primary mechanisms, and understand where it excels and where it falls short.

If you're curious about implementing this privacy-enhancing method in your projects, you're in the right place.

Let’s jump right in.

Definition of Differential Privacy

Differential privacy is a mathematical framework for ensuring the privacy of individuals in datasets. It can provide a strong guarantee of privacy, allowing analysts to examine data without revealing sensitive information about any individual in the dataset.

Professionals use this method as it prevents linkage attacks, making it a good choice for protecting individual data in various scenarios. This assurance of privacy is crucial in research contexts where ethical considerations matter.

While DP has good sides, it introduces noise to protect individual privacy, which can reduce result accuracy and obscure meaningful patterns, especially in high-fidelity data applications.

Additionally, it faces scalability challenges with large datasets and requires a delicate balance between privacy and data utility, making it more effective when integrated with other techniques.

In terms of protecting data in use and data security, efficiency, and precision, we could summarize DP's pluses and minuses in the table below.
Security Techniques
The concept of differential privacy was first introduced in 2006 by Cynthia Dwork and Frank McSherry, et al. in two papers titled "Calibrating Noise to Sensitivity in Private Data Analysis” and “Differential Privacy”.

In these papers, Dwork and McSherry proposed a mathematical framework for formally defining and achieving privacy in data analysis, which they called "differential privacy."

According to their definition, the presence or absence of any individual record in the dataset should not significantly affect the mechanism's outcome.

We call "mechanism" any computation that can be performed on the data. Differential privacy deals with randomized mechanisms, which are analyses whose output changes probabilistically for a given input.

Thus, a mechanism is considered differentially private if the probability of any outcome occurring is nearly identical for any two datasets that differ in only one record.
Definition of Differential Privacy
In a differentially private system, the output of a function doesn’t vary whether a record is present or absent from the queried system. This definition of differential privacy has since become the standard for measuring privacy in data analysis.

One key feature of differential privacy is that it provides a privacy guarantee that holds regardless of what an adversary knows or does when attacking the data.

In this context, an adversary is a person or entity that is trying to learn sensitive information about individuals from the output of a data analysis.

The privacy guarantee holds even if the adversary has unlimited computing power and complete knowledge of the algorithm and system used to collect and analyze the data.

So even if the adversary were to develop new and sophisticated methods for trying to learn sensitive information from the data, or if new additional information becomes available, differential privacy would still provide the exact same privacy guarantee, making it future-proof.

Differential privacy is a flexible concept that can be applied to various statistical analysis tasks, including those that have yet to be invented. As new statistical analysis methods are developed, differential privacy can be applied to them to provide privacy guarantees.

At the same time, differential privacy alone is too weak in an enterprise environment where data utility is the most important factor.

It’s because when we talk about AI projects that require a significant amount of accurate data and are sent internationally to countries with varying jurisdictions, this method doesn’t guarantee high utility.

This means that it would be hard to build a high-performance data product when you need accurate data.

To better understand the depths of this method, the next section focuses on “how” it works. There, we look at how differential privacy achieves this through the addition of noise that ensures that any individual's information is not disclosed while still allowing for the

How Differential Privacy Works

Several mechanisms are commonly used in differential privacy to ensure the privacy of individuals in datasets.

One of the most commonly used mechanisms to answer numerical questions is the addition of calibrated noise: adding enough noise to the output to mask the contribution of any possible individual in the data while still preserving the overall accuracy of the analysis.

One concrete example of adding noise for differential privacy is the Laplace mechanism.

The Laplace Mechanism

In this mechanism, a data scientist adds noise to the output of a function. The amount of noise depends on the sensitivity of the function and is drawn from a Laplace distribution.

The sensitivity of a function reflects the amount the output can vary when the input changes. More accurately, it is the maximum change that can occur in the output if a single person is added to or removed from any possible input dataset.

The concept of sensitivity is important because it helps to determine the amount of noise that a data scientist needs to add to a function's output to protect individuals' privacy in any possible input dataset of that function. The larger the sensitivity, the more noise must be added.
The Laplace Mechanism
The amount of noise that needs to be added using the Laplace mechanism to apply differential privacy is determined by the sensitivity of the function. This often requires adding significant noise, which can decrease the accuracy of the results.

For example, in the healthcare industry, we have this database containing people with a particular medical condition. We want to release the number of people in a city with that condition while preserving their privacy.

If only a few patients in the city have the condition, and if someone knows a person is in the database, this could reveal that this person has the condition and their medical status.

We can use the Laplace mechanism as differential privacy mechanisms to add noise to the count of people with that condition to prevent any individual from being identified.

The amount of noise added to the data would be related to the sensitivity of our function. Since each patient's contribution can change the result of the count by a maximum of one, our sensitivity is equal to one, and we would add noise accordingly.

By adding this noise, we can ensure that the released count is differentially private.

Other Mechanisms: Randomized Response and Perturbations

Another common mechanism of differential privacy is known as randomized response.

It involves asking individuals to respond to a "yes" or "no" question in a randomized manner, with a certain probability of giving a truthful answer and a certain probability of giving a random response.

For example, we want to collect data on sensitive topics such as criminal behavior or political views.

To protect the privacy of individuals in the dataset, we could use the randomized response mechanism by asking them to respond to a "yes" or "no" question with a 50% probability of giving a truthful answer and a 50% probability of giving a random response.
Other Mechanisms: Randomized Response and Perturbations
The randomized response mechanism is a privacy-preserving technique that involves asking individuals to respond to a question in a way that protects their privacy while still allowing their responses to be collected and analyzed.

Randomized responses allow for collecting data while still protecting individuals' privacy by ensuring that an individual's responses can be claimed to be the product of chance rather than their true response.

This technique introduces plausible deniability: individuals may always claim that the mechanism forced them to lie.

While this benefits individuals, it could negatively impact projects where enterprise teams build AI products. The noise and inaccuracy this method introduces reduce the quality and precision of the data.

This reduction in data fidelity is problematic for building products based on AI components, highlighting the necessity for a comprehensive data security platform that integrates multiple privacy techniques to achieve both privacy and accuracy.

Another important aspect of our Gen AI era is that DP introduces bias into the data if not used carefully. Bias in data is a big topic for a reason—we all saw the consequences of systems being unethical or unfair as they were fueled by skewed, inaccurate data.

For example, if the probability of giving a truthful answer is too low, the data may not be representative of the population thus not useful for data projects where you need statistically significant data.

Now, let’s talk about the details of DP.

The Privacy Budget

A very powerful and distinguishing feature of differential privacy is the ability to quantify the maximum amount of information that can be disclosed. This upper bound on “information leak” is called the privacy budget.

The privacy budget is typically set using a mathematical formula known as the "privacy loss function," which determines the amount of noise that needs to be added to the data to achieve a certain level of privacy.

Alternatively, it can be calculated a posteriori, after adding noise to the data, to assess the level of privacy.

In the next section, we present some examples of the applications of differential privacy.

Differential Privacy Applications

Here is a concrete example of using differential privacy in a medical dataset. Suppose we have a dataset containing individuals' medical records, and we want to release the number of individuals in the dataset with a certain medical condition while preserving their privacy.
Here is a concrete example of using differential privacy in a medical dataset.

Suppose we have a dataset containing individuals' medical records, and we want to release the number of individuals in the dataset with a certain medical condition while preserving their privacy.

Releasing the information without any noise or perturbation could lead to identifying individual patients in the dataset.

Suppose the dataset is small or the medical condition is not evenly distributed. In that case, it may be possible to identify individual records based on the released count of patients with the condition, with or without additional information or assumptions.

To achieve differential privacy in this scenario, we could follow these steps:

  • After having calculated the privacy budget, we need to determine the sensitivity of the function, which corresponds to how much a single individual can affect the output of the function in the worst case. In that case, the sensitivity is one because adding or removing a single patient from a data set, be it with or without this medical condition, can change the result of the count by at maximum one.

  • Next, we need to choose a differentially private mechanism for adding noise to the output of the function. In this case, we could use the Laplace mechanism.

  • Once we have chosen the Laplace mechanism, we can apply it to the function to add noise and protect the privacy of individuals in the dataset. It would produce a noisy version of the count of patients with the condition that is differentially private.

  • Finally, we can release the noisy version of the count of patients with the condition, which will not reveal any information about individual records in the dataset. We have applied differential privacy and ensure the protection of the data thanks to differentially private mechanisms.

Real-life Examples of Differentially Private Analysis

Differential privacy found several applications as a tool to protect the privacy of individuals while still allowing for some sort of insights from data. While these cases are hands-on, you should still keep in mind that for most complex use cases, this method wouldn’t work alone.

Let’s see the examples!

The first one is the U.S. Census Bureau, which uses differential privacy to protect the privacy of individuals while still allowing for the release of aggregate statistics about the population.
Real-life Examples of Differentially Private Analysis
Creating differentially private data for the 2020 Census redistricting files. (Source: US Census Bureau as reproduced on Differential Privacy and the 2020 US Census by Simson Garfinkel)

Differential privacy can also support collecting data about how users interact with a product or service, such as which features are used most often, without revealing personal information about individual users.

For example, using this method, Apple collects data about how users interact with their devices, such as which features are often used.

The University of California, Berkeley uses differential privacy to study the spread of infectious diseases, such as influenza and COVID-19, without revealing the identities of individual patients.

The Healthcare Cost and Utilization Project (HCUP) uses differential privacy to study healthcare utilization and costs across the United States while still protecting the privacy of individual patients.

Differential privacy can also support the generation of synthetic data for use in data-driven decision-making, such as in public policy or business planning, without revealing sensitive information about individuals.

In the next section, we explore the topics of differentially private algorithms, machine learning, and synthetic data.

Differentially Private Algorithms and Machine Learning Models

Differentially private machine learning algorithms are designed to protect the privacy of individuals in the training data. They use techniques from differential privacy to add noise while still allowing the algorithm to learn from the data and make accurate predictions or decisions.

However, they often struggle with maintaining accuracy and efficiency, making them less practical for large-scale machine-learning projects.

As for applications for smaller projects – you can use this method in machine learning algorithms in several ways. One common approach is to add noise to the data during the training process.

Other approaches involve using differential privacy to protect the algorithm's outputs, such as the predictions or decisions made by the model, or partitioning the data and aggregating the response of a set of models, each trained on a single data partition.

The differential privacy training could prevent the machine learning algorithm from revealing sensitive information.

For example, preventing an algorithm trained to predict the likelihood of a patient developing a certain medical condition to reveal sensitive information from records of patients who have been treated for the medical condition in the past.

Differentially-private Synthetic Data

Differentially-private synthetic data is a type of synthetic data that is generated using differential privacy techniques.

Synthetic data, which is generated by a computer algorithm instead of being collected from real-world sources, has many applications, such as in testing machine learning algorithms or privacy-preserving data analysis.

To generate synthetic data with a differential privacy guarantee, a computer algorithm creates data similar to the original dataset but with the added property of differential privacy.

It means that noise has been added while training the generative model, making it extremely difficult to determine the individual records in the original dataset from the newly generated data.
Differentially-private Synthetic Data
To generate differentially private synthetic data, the synthetic generation models learn the original data distribution with a differentially private algorithm to benefit from the theoretical guarantees that differential privacy provides.

After setting a privacy budget, the algorithm can generate synthetic data. The resulting data will have the property of differential privacy, meaning that it becomes harder to determine the individual records in the original dataset from the newly generated data.

This method, using differential privacy, has certain advantages over standard synthetic data. For example, it can provide privacy-preserving data analysis or enable data sharing between organizations without risking the privacy of individual data.

This layer of protection is critical in a context where synthetic data generation is gaining such popularity. This rise in popularity is notable due to the variety of its purposes, such as training machine learning algorithms, testing algorithms, or sharing data with third parties.

Yet, potentially revealing sensitive information through data privacy breaches may expose the organization or individual to regulatory consequences.

Many data privacy laws and regulations require organizations to protect individuals' privacy when using their personal data, and differential privacy is one way to do this.

Limitations of Differential Privacy

We already briefly mentioned the shortcomings of different privacy as a standalone technique. Let’s look at this method's disadvantages now.

The main one is the trade-off between privacy and utility. Differential privacy adds noise to data to protect the privacy of individuals. Still, this noise can also reduce the utility of the data, making it less accurate or useful for certain types of analysis. This trade-off can be difficult to manage and requires careful balancing to balance privacy and utility.

The lack of standardization and agreement on best practices also remains a challenge. Differential privacy is a relatively new field, and there is currently no standardized approach to implementing differential privacy.

It can make it difficult to compare and evaluate differentially private algorithms and limit the ability to develop a common framework for differential privacy.

Further Reading on Differential Privacy

Many resources are available online to learn more about differential privacy. The community-managed Differential Privacy . org is a great place to start. There are also many research papers and articles on the topic, as well as tutorials and courses that provide a more in-depth understanding of the concepts and techniques involved.

For an excellent hands-on guide, see also Programming Differential Privacy, and for more examples of real-life applications, see "A list of real-world uses of differential privacy"

Differential Privacy: What’s Next

And that would be it. As you can see, the method is still new but has undergone rapid development over the years.

In some cases, the method is fine. For example, it’s a valuable opportunity in healthcare, where data is often sensitive and personal. It can also improve public policy, healthcare outcomes, and decision-making.

The catch is that if you have a more complex use case, DP is valuable only as part of a bigger data security platform where you can use various methods for advanced data projects. So if you’re really trying to bridge the gap between data value and compliance, DP itself won’t help.

Currently, the main research tracks in the field include the development of algorithms for different types of differentially-private data analysis tasks and improving the utility of differentially-private algorithms.

We’ll be observing how differential privacy and other privacy technologies develop. If you’re also curious about these topics, sign up for our Anonos Data Leaders’ Digest newsletter. There, we share exclusive content along with expert opinions.
Disclaimer: This blog post was written with input from ChatGPT, the large language model trained by OpenAI.

For an overview of the writing process, see this Twitter thread. The privacy researchers at Anonos, Matteo Giomi, and Nicola Vitacolonna reviewed the content.

Thank you to Ricardo Carvalho, a Computer Science PhD student at Simon Fraser University (SFU) and expert on Machine Learning, Generative Adversarial Networks (GANs), and Differential Privacy, for his input and feedback on the post.