The cybersecurity industry has undergone drastic changes in the face of artificial intelligence, creating more uncertainty for organizations. According to a World Economic Forum cybersecurity report, the use of AI and machine learning has become a top security concern, and the use of generative AI has raised fears around data leaks. Thankfully, an approach known as differential privacy can help institutions keep up with AI-powered cyber threats and adopt AI technologies themselves without compromising data privacy.
Differential Privacy, Explained
Differential privacy is a mathematical definition of privacy used to calculate how much noise needs to be added to a data set to properly secure data. According to this standard, data is considered private only if an algorithm cannot discern whether an individual’s data is contained in a database based on an output.
Differential privacy determines the appropriate amount of noise to add to a data set to keep data private. By introducing enough randomness, differential privacy can make it mathematically impossible to identify specific individuals’ data within a database, while ensuring the data can still be used to produce accurate results.
As the threat landscape evolves, understanding how to apply differential privacy will become vital for defending personal data against increasingly unpredictable cyber attacks.
What Is Differential Privacy?
Differential privacy is a mathematical standard used to protect personal information by adding “noise” to a data set, with noise being small, random changes to the numbers so that no one person’s information can be identified. Suppose an algorithm analyzes a data set to calculate its statistical traits like the mean, median and variance. According to differential privacy, data is considered private only if the algorithm cannot determine whether a specific individual’s data is included in the data set based on the output.
There are two types of differential privacy used to shroud personal data in noise:
- Local Differential Privacy (LDP): LDP adds noise to every individual data point before an algorithm analyzes them.
- Global Differential Privacy (GDP): GDP adds noise once to the algorithm’s output after the data points have already been processed.
In either case, enough noise is introduced so that the addition or removal of an individual’s data doesn’t significantly impact the algorithm’s output. The output is just as likely to have come from a database without an individual’s information as it is from one with it. This way, characteristics about the data set can be shared without exposing anyone’s identity.
How Does Differential Privacy Work?
While adding noise to a data set prevents an individual’s data from being identified, too much noise could affect the accuracy of an algorithm’s outputs. To control the level of noise, differential privacy relies on two main parameters:
- Epsilon (ϵ): ϵ is a parameter that measures what’s known as “privacy loss,” or how much the output is impacted by an individual’s data being added or removed from the data set.
- Delta (δ): δ is a parameter that represents the probability that the privacy guarantee determined by ϵ will fail, essentially determining the likelihood of a data breach.
Differential privacy adds ϵ to a data set to create a degree of randomness in the data points, so no one individual’s data can be discerned among the group. Still, ϵ must balance individual privacy with the accuracy of an algorithm’s outputs to ensure the data remains useful. A higher ϵ value means stronger privacy, but also weaker accuracy. δ can be introduced to further manage the noise level, with a higher δ value representing a greater risk of a breach occurring.
ϵ and δ values can be adjusted accordingly to account for the sensitivity of the data and the level of privacy failure permitted. For example, a database with more personal information that could easily be used to identify someone will require increased privacy standards.
Why Is Differential Privacy Important?
As AI and machine learning algorithms are used to process more data across a wider range of industries, the need to regulate them has become more urgent. The European Union has led the way with its AI Act, but there are additional data privacy regulations that organizations need to be aware of and comply with.
Even if AI regulations undergo major changes in the U.S. and other countries, companies may fail to earn consumers’ support if they don’t address online privacy concerns. And well-meaning efforts like scientific research still require an element of anonymity to protect participants’ data. Implementing differential privacy enables these businesses and organizations to apply AI solutions while maintaining the trust of all parties involved.
How Is Differential Privacy Used?
Differential privacy enables data to be shared in many sectors without putting individual privacy at risk, including government, healthcare and marketing.
Conducting a Nationwide Census
Every ten years, the U.S. government gathers sensitive demographic information for the census, including details related to race, sex, occupation and housing status. As part of its “disclosure avoidance” efforts, the government has adopted differential privacy to continue collecting personal data while obscuring the identities of survey participants.
Analyzing Patient Data
Clinical trials involve gathering sensitive personal health data on participants to better understand diseases, develop personalized treatments and monitor the effects on patients. By using differential privacy, researchers can hide each participant’s identity without impacting the accuracy of a study’s results.
Creating Synthetic Data Sets
When creating synthetic data, users can feed a data set into an algorithm that adds noise via differential privacy. This allows users to work with data that reflects the statistical traits of the original data set but doesn’t expose details that can be used to identify individual data.
Learning From Mobile User Habits
Mobile providers can also use differential privacy to shield individuals’ identities when compiling information on user behavior. For example, Apple employs differential privacy when collecting user data, so it can still learn from users’ habits to improve its products without raising privacy concerns.
Assessing Advertising Campaigns
Marketers can craft and analyze ad campaigns without revealing their target audience’s personal information. When assessing ad clicks, the number of people who signed up for a new product and other metrics, teams can use differential privacy to hide individual demographic details and focus solely on studying general target audience interactions with ads.
Advantages of Differential Privacy
By putting in place the proper measures to keep data private, differential privacy actually facilitates the wider sharing of data.
Enhanced Protection
Differential privacy helps defend against a range of cyber attacks, even if those using data privacy tools are unaware of these attacks. Whether a privacy guarantee holds or fails doesn’t depend on the type of privacy attack, resulting in extra protection against malicious actors.
Increased Transparency
Traditional privacy methods often hide how data has been altered for security reasons. Meanwhile, differential privacy doesn’t require this step, allowing users to share how data has been transformed and build trust and transparency around their findings.
Greater Customization
The ϵ and δ parameters enable users to adjust the degree of differential privacy according to the needs of the situation. Users can establish standards to secure data while still preserving the accuracy of the results.
Broader Accessibility
Differential privacy makes it possible to share data with the broader public without exposing personally identifiable information. Researchers can then freely share the results of scientific studies and experiments, giving broader audiences access to this knowledge.
Drawbacks of Differential Privacy
Differential privacy may deliver a higher level of security than other techniques, but it still has some serious limitations.
Short-Lived Security
When running an algorithm multiple times over the same database, privacy loss increases and differential privacy’s privacy guarantee grows weaker. This complicates the delicate balance between accurate results and anonymous data.
Lower Accuracy
The practice of adding noise to a data set may not have much impact on a large data set, but it can seriously affect smaller ones. As a result, differential privacy may not be well-suited for small data sets when accuracy is a priority.
No Individual Insights
Because of the noise it introduces to data, differential privacy makes individual data unidentifiable. This can become an issue in certain situations, such as a financial institution trying to track down an individual suspected of fraudulent activity.
Lack of Guidelines
There is no universal agreement over the ideal value of ϵ to achieve the right balance between data being protected while still being useful. It’s up to users to determine their target value for ϵ to properly secure data in various scenarios.
Frequently Asked Questions
What is the goal of differential privacy?
The goal of differential privacy is to make personal data shareable without exposing the individual’s identity. To accomplish this, differential privacy introduces enough noise so that it’s difficult to pick out individual data within a database without impacting the accuracy of the insights gleaned from the data.
How does differential privacy differ from data anonymization?
Data anonymization protects individual privacy by altering or removing personal data within a database. On the other hand, differential privacy involves introducing an amount of noise to obscure individuals’ data within the data set. It doesn’t directly change the data. Instead, it adds a degree of randomness to make it difficult to discern individual information.
