Differential Privacy and Applications
A data breach can lead to fraud, identity theft, and millions of dollars in damages, not to mention a soiled professional reputation. Breaches are on the rise—in fact, the Verizon 2021 Data Breach Investigations Report found 5,258 confirmed data breaches across 20 industries.
With sensitive data on the line, it’s no surprise that the federal government has enforced privacy laws since the 1970s. A lot has changed since the first privacy law—in a modern society that runs on data, how can companies and individuals ensure their privacy?
As organizations collect more data, they must provide more data privacy as well. One security method gaining popularity for its unique handling of security is differential privacy, and applications of this method are widespread. It allows complex data analysis without risking privacy loss.
Using Differential Privacy to Harness Big Data and Preserve Privacy
Differential privacy is a system of sharing data by describing patterns in a dataset while obscuring identifying information. For instance, any number of agencies may publish statistical or demographic data, but with differential privacy in place, it’s impossible to tell how any specific individual contributed. The idea is that a researcher would receive the same query result whether the dataset included a specific piece of data or not.
In short, a system has differential privacy if a data scientist cannot use the data to identify an individual.
Using Differential Privacy for Security Measures
Differential privacy works by introducing randomization, or noise, into datasets. The most common way to achieve differential privacy is through a mathematical formula called the Laplace mechanism, which will alter a controlled amount of data. A data scientist cannot identify any individual response as long as they don’t know which data changed. However, a data scientist can factor the randomization into their calculations to draw accurate conclusions about the entire dataset.
For example, imagine a simple yes or no survey. However, before respondents submit their answers, they flip a coin. If it’s heads, they submit their answers without alteration, but if it’s tails, they flip again. On the second coin toss, heads tells them to answer yes and tails means they answer no—regardless of their original answer.
Only a small percentage of the data changes after two coin flips, but each data subject could plausibly deny that their real answer made it into the dataset. Differential privacy works like this on a much bigger scale. It uses an algorithm, similar to a pair of coin flips, to randomize a fixed amount of collected data.
Using Differential Privacy to Harness Big Data
Big data refers to the large, complex data sets collected by businesses daily, and it shapes our modern world. Everything from machine learning and Google AI to the recommendations on your Amazon home page benefits or results from big data collection. By analyzing the terabytes of data collected every day, companies can improve their products, customer service, and marketing campaigns. However, with strict information privacy laws in place, organizations must be careful about how they harness big data.
Differential privacy removes the biggest obstacle from data collection: privacy risk. With quantifiable security, companies and organizations can continue to gather and use data to advance their industries.
For example, big data mining companies like Google and Apple protect private information from searches by using differential privacy tools. They can still analyze the data and draw insights that improve their products, services, and marketing, but their clients’ sensitive data remains safe.
One type of differential privacy, synthetic data, harnesses big data into machine learning. A synthetic dataset is artificially generated, but it has properties of the original data. It is secure because it reflects the raw data without disclosing it. It’s helpful for exploratory research and training machines.
What Are the Challenges and Limitations of Differential Privacy?
No privacy measures are perfect; differential privacy faces several challenges and limitations.
Challenges of Differential Privacy
One challenge is determining the optimal randomization level. It’s difficult to balance privacy with usefulness. Too much randomization leads to inaccurate data, but too little randomization can’t ensure perfect privacy. Furthermore, differential privacy is still new; it currently lacks overall industry standards and regulations.
Another challenge is that differential privacy, while simple in concept, is difficult to implement. It takes high computational power, extra time and personnel to manage the algorithm, and a lengthy period for conducting tests (PDF, 3 MB). Therefore, not every organization has the resources to take advantage of differential privacy.
Limitations of Differential Privacy
The first limitation is that it can’t protect every dataset. Small data can’t use randomization because the results will be too inaccurate. Similarly, there are some situations that require individual level analysis. For instance, if a bank applies noise to its system, it may not identify fraudulent activity.
Secondly, datasets that use common random noise–based approaches to differential privacy can’t be anonymous forever. Every time a data scientist sends a query, the level of anonymization reduces. This is because the amount of noise is fixed—averaging over time can filter it out.
In simple terms, the more the data is accessed, the easier it is to reconstruct the noise. Therefore, analysts can only access data secured with differential privacy a fixed number of times according to how randomized the data is. Analysts call this the privacy budget, and it limits how useful the data is before it’s no longer considered anonymous.
Overcoming the Challenges and Limitations
Organizations can overcome many of the challenges and limitations of differential privacy through planning and balancing their needs. Researchers can overcome the privacy budget by understanding the query limit in advance. They could even choose a different approach based on their goals. For example, they may prefer to use synthetic data over another method if accuracy is less important than the query limit, such as for testing a hypothesis.
In addition, as the use of differential privacy grows, industry-spanning standards increase as well. Differential privacy is already in high demand, and accessible tools are on the way. Policymakers will likely match the pace of this developing technology and create guidelines for the randomization of data that ensures a high enough level of privacy.
Differential privacy is a relatively new way of securing sensitive data. As research continues and technology advances, computer scientists will overcome remaining challenges like the privacy budget.
Advantages of Differential Privacy Over Other Privacy Measures
Investing in privacy has a high rate of return for companies and organizations. In fact, according to the 2022 Cisco Data Privacy Benchmark Study (PDF, 6 MB), 60 percent of companies reported significant benefits after investing in privacy. These include increasing customer loyalty and even a greater level of operational efficiency.
Differential privacy has clear advantages over other privacy measures. Most consumer data aggregators choose differential privacy methods because they can customize the level of privacy. They can provide extra security to the most sensitive data by adjusting the amount of random noise they add to the dataset. Other security methods attempt to customize privacy by masking data, but it’s much easier to simply adjust a parameter.
Similarly, companies can quantify their level of safety because differential privacy uses mathematical formulas. While other methods of security can only make claims about how private a dataset is, differential privacy can back up those claims with mathematics. This is a huge legal advantage as privacy laws evolve.
As differential privacy becomes more commonplace, it may also ease the public’s opinion on data collection. Pew Research reports that 81 percent of Americans believe the potential risks of data collection outweigh the benefits. In addition, 79 percent express concern about how companies collect and use data. Differential privacy offers a privacy guarantee that people may prefer over the current, less reliable methods.
Comparing Different Privacy Measures
Another type of privacy measure to consider is anonymization. It’s a popular method that data collectors have been using for years. It removes identifying information, like names, from a data set.
Anonymizing data is no longer enough; in fact, the General Data Protection Regulation in Europe considers even pseudonymous data to be personal data. Methods like linkage attacks, which connect external datasets to fill in masked fields, can reidentify anonymous data.
Aggregating data is similar; this method shows only summary information about collected data instead of anonymized raw data. However, even statistical information like mean and count can reveal whether a person’s data is in the set. All an attacker needs to do is calculate what the data would look like with and without a person’s information and compare it to the true values.
Differential privacy is different. It treats every part of the data as sensitive. Rather than just masking names and identifying details, the algorithm could randomize the survey responses and other facts and figures as well. Privacy like this is practically invulnerable to linkage attacks. It would take too much time and effort to remove noise from the results.
Examples of Differential Privacy in Use Today
Using differential privacy methods allows organizations and companies to share valuable data without breaking privacy laws. Sharing data helps analysts draw more complete conclusions from greater amounts of raw data. This means that problems get solved faster; safely shared data can improve everything from traffic to health care and more.
Industries Using Differential Privacy
Any industry that collects and analyzes data can benefit from differential privacy, but some industries are ahead of the game. The big players in the information technology field, like Google and Apple, heavily rely on differential privacy. In short, it’s a game changer in the worlds of research, analytics, and statistics.
The health care industry has started using it as well. For instance, during the COVID-19 pandemic, public health data has been of utmost importance for keeping people safe and informed. Australia developed the COVID-19 Real-Time Information System for Preparedness and Epidemic Response (CRISPER) to fulfill this need. CRISPER uses differential privacy to keep personal data, like age and comorbidities, safe when reporting cases.
Examples of Differential Privacy in Use
Countless organizations have already found plenty of practical use cases for differential privacy. Most notably, in 2020, the US Census Bureau concealed demographic census data using differential privacy. This protected the detailed, sensitive information of the entire American population.
Uber uses a differential privacy method based on elastic sensitivity to protect its drivers and riders. As Uber data scientists query their database to perform analysis, the system limits the amount of personal information revealed to ensure every individual’s anonymity. Uber can analyze traffic patterns and even calculate revenue from raw data without distinguishing any single person’s data.
Another practical application of differential privacy is preventing data breaches; data protected in this way is much less attractive to attackers. According to IBM’s 2021 Data Breach Report, the cost of data breaches rose to $4.24 million. This is the highest it has been in the seventeen-year history of the report. Better security (PDF, 8 MB) coupled with the best data privacy practices can keep many companies from becoming part of the next set of statistics.
See More of Differential Privacy in Action
Our modern world depends on collecting and analyzing big data, but privacy protection should always be the top priority. Differential privacy allows organizations to safely collect data without risking the sensitive information of individuals. All data mining industries can keep using data analysis to improve their products or services while avoiding dangerous data breaches with the help of differential privacy.
Keep learning about differential privacy and applications that this useful security method has in the world of information technology! Find out more about differential privacy in deep learning here, and stay up to date on the topic by exploring more of the IEEE site now.
Interested in joining IEEE Digital Privacy? IEEE Digital Privacy is an IEEE-wide effort dedicated to champion the digital privacy needs of the individuals. This initiative strives to bring the voice of technologists to the digital privacy discussion and solutions, incorporating a holistic approach to address privacy that also includes economic, legal, and social perspectives. Join the IEEE Digital Privacy Community to stay involved with the initiative program activities and connect with others in the field.