Keeping data private and secure is difficult. Traditional privacy-preserving techniques, such as cryptography, can’t resolve this since they prevent tech companies from accessing the data. Differential privacy has evolved into an advanced cybersecurity concept that protects personal data far better than traditional methods.
Differential privacy is a powerful concept and approach to preserving privacy that doesn't rely on encryption. Differentially private techniques can strip data of your identifying characteristics so that no one (hackers, government agencies, or even the company) can use or collect it without compromising your privacy.
What is Differential Privacy (DP)?
Differential privacy guarantees that any company can use only aggregated information about you and your behavior while maintaining your privacy.
Differential privacy is an approach to data analysis applied in many fields. It is designed to address privacy concerns. Under normal circumstances, including your information in a database can violate your privacy.
Even if your data is anonymized and your identifiers stripped, it may still be linked to your identity through statistical analysis. The idea behind differential privacy is that you cannot violate a person’s privacy if their data is not in a database.
With this in mind, differential privacy aims to grant individuals in a database the same level of confidentiality as if their data were entirely excluded. A system is differentially private if the data is structured so that you cannot determine whether a particular individual participated.
Differential privacy comes in two different models:
- Global Differential Privacy
- Local Differential Privacy
The main difference between the two models is how noise is added and who has access to the raw data. In both models, noise is a slight inaccuracy added to the data to prevent it from being identifiable and keep it anonymized. Below, we further explore how the two models work.
Global Differential Privacy
In this model of Differential Privacy, an aggregator or research organization has access to your raw data. What is an "aggregator"? It's a service or research organization that collects data about individuals. The aggregator takes this data and transforms it using a differentially private mechanism, where each individual sends their data to the aggregator without adding noise. In this model of Differential Privacy, an aggregator or research organization has access to your raw data.
At the end of the process, the differentially private mechanism is applied only once. Then the aggregator can share it with the data scientists.
The primary advantage of global differential privacy is accuracy, as only a minimal amount of noise is required to obtain valuable results. The aggregator can be an untrusted company or government. Yet individuals must trust the aggregator enough to share their raw data, which might be difficult given that all the information is collected in one place.
Local Differential Privacy
In this model of differential privacy, a central aggregator or research organization does not have access to individuals' raw data. The individuals themselves add noise and differentially private mechanisms to their data before sharing it with the aggregator. Then the aggregator can compute statistics and share the results with the data scientist without introducing noise, and it doesn't need to be differentially private.
The main advantage of this model is that your raw data is not compromised, as you are already protecting it. Your information is secure even if the data aggregator is malicious. The downside of this process is that the output data is not as accurate as that of the Global Differential Privacy model, as it already contains significant noise. In this model, aggregators require many more individuals than in the global differential privacy approach to obtain valuable results.
Why does Differential Privacy Matter?
Most people confuse Security with Privacy: Security controls “Who” can access the data, while Privacy is more about “When” and “What” can be accessed. In short, “You can’t have privacy without security, but you can have security without privacy.”
Differential Privacy matters because it provides better and more robust data privacy. Here are some reasons why DP matters:
- It protects your information as if it were not used in the analysis at all.
- It ensures that your data does not reveal any personally identifiable information (PII) about you.
- It essentially masks your contribution to the analysis, making it impossible to deduce any information specific to you.
- A hacker who has no information about your private data cannot compute a function of the output to reduce its differential privacy.
- It provides privacy guarantees regarding the cumulative risk from successive data releases.
How does Differential Privacy Work?
Ideally, differential privacy will enable companies to collect information while reducing the risk that it will be accessed or used in ways that harm human rights.
For example, suppose a company wanted to demonstrate the most suitable way to buy groceries. They tracked 100 people who regularly purchase groceries and whether they go to the store or get the groceries delivered.
Instead of sharing specific information about individuals buying groceries, the company shares the aggregate data it has collected over time. Anyone viewing the results might know that 75 out of 100 people prefer to go to the store, but they don’t know who they are.
Let’s go back to the example of buying groceries. If the company knows the identities of those included in the study but doesn’t know who preferred which mode of grocery shopping, you might assume privacy is protected. But this may not be the case.
Imagine someone viewing the data wants to make sure Mike prefers going to the store rather than having groceries delivered. They have obtained background information on the other 99 people in the study, which indicates that 25 prefer to have their groceries delivered and 74 prefer to go to the store. Therefore, they can conclude that Mike, the 100th person in the database, is the 75th person who chooses to go to the store.
This type of attack is called a differentiated attack. It is tough to defend against, as you cannot control how much background knowledge someone can obtain. The primary purpose of Differential privacy is to protect against this type of attack.
Someone knowing your grocery-buying habits might not sound very serious, but if you replace it with any confidential information, it can lead to a severe privacy breach.
“Differential privacy guarantees that even if someone has complete information about 99 of 100 people in a data set, they still cannot conclude the information about the final person.”
The primary purpose of differential privacy is to add random noise to the aggregate data. In the above example, you may say the number of people who prefer to go to the store is 74 or 76, rather than the exact number 75. This inaccurate number can ensure Mike's privacy is protected, but it will have a minimal effect on the pattern, i.e., around 75% of people prefer to go to the store. This type of privacy is called differential privacy.
What are the benefits of Differential Privacy and the Rainfall system?
Differential privacy is the mathematical definition of privacy. It is possible to quantify the security or anonymity of information in a dataset. “Anonymous” means that a person or group cannot be identified. Rainfall works with a similar data set, as we do not collect, store, or share your personal information.
The Rainfall system uses the differential privacy algorithm to protect your private information. Rainfall will access only the data you decide to share and use advanced edge-based AI algorithms and decentralized blockchain technology to aggregate, contextualize, and generate intelligence from your data. We call this intelligence Real-time Social Insights (RSI). These Insights are generated directly on your phone, and your personal data never leaves your device.
RSIs are purchased by businesses, organizations, and governments that benefit from the aggregated insights Rainfall’s AI produces. The revenue generated from these insights is automatically distributed back to you in your Rainfall wallet in the form of Raindrops (native cryptocurrency of Rainfall). It enables you to earn a continuous, meaningful income with minimal to no participation in the process other than providing consent.
Final Thoughts
Differential privacy is an exciting concept that could help move you away from a world where almost every moment of your life is tracked. Big tech companies are increasingly gaining access to your personal and social interactions to collect vast amounts of data daily without your permission. At the same time, privacy violations in cyberspace regularly make front-page news.
Rainfall uses differential privacy algorithms that enable us to create insights in a privacy-preserving manner. Rainfall edge-based AI algorithms are transparent about “when” and “what” kind of data is aggregated. This approach enhances Rainfall’s ability to protect the individual's privacy in the data.
