Organizations need to access useful information about users or consumers while protecting their privacy. This, in addition to threats like data breaches, has led to a reconsideration of privacy from governments, organizations, and companies. One result of this reconsideration is the development of formal privacy models such as Differential Privacy. Please keep reading to learn more about differential privacy and how companies can use it effectively to bolster privacy and access to useful consumer information.
What is Differential Privacy?
Sharing aggregate information about their user’s habits while maintaining the privacy of individual users is possible for tech companies with differential privacy. It was primarily developed to assist companies and organizations working with a lot of private information with the ability to share pertinent information while still protecting privacy.
The definition of differential privacy is mathematical in nature. It is a robust mathematical definition of privacy within the context of machine learning and statistical analysis. Differential privacy sets criteria for privacy protection, and tools that work with and analyze sensitive information must satisfy these criteria before accessing the sensitive information.
How Does Differential Privacy Work?
At its most practical, differential privacy allows companies or other institutions to gain value from personal information while protecting their users’ privacy. Here are some very broad examples.
- Apple uses differential privacy to gather data anonymously from its iPhone, iPad, and Mac users.
- Differential privacy is used by Amazon to access their customers’ individual shopping preferences while protecting any sensitive information about their buying history.
- Facebook uses differential privacy to collect behavioral data from its users without violating the nation’s privacy policies for targeted ad campaigns.
Differential privacy accomplishes these feats in several ways.
- A privacy budget or privacy loss parameter is introduced in a dataset. This parameter influences the amount of noise or randomness that is introduced into the raw dataset.
- Additional randomness is added to the dataset by randomly changing answers based on a “coin flip.” (Of course, real-world applications of differential privacy use more complex algorithms than a coin flip.)
- For large enough datasets, the information is still accurate when it comes to aggregate measurements. But, each individual represented in the dataset can, with the randomness factor, deny their answers with plausibility. This process protects user privacy.
Characteristics of Differential Privacy
1. Privacy loss quantification
Privacy loss can be measured under a differential privacy mechanism and algorithms. This enables comparisons among different methods. Privacy loss becomes controllable, and a trade-off of privacy loss and accuracy of the generic information is established.
2. Composition
Being able to quantify loss gives the control and analysis of aggregate privacy loss through multiple computations. And in addition, understanding how differentially private mechanisms behave allows the planning and analysis of compact differentially private algorithms from simpler building blocks of differential privacy.
3. Group Privacy
Privacy is not simply valuable to individuals. Groups, like families, also value and need privacy. Differential privacy gives control and analysis of privacy loss gained by groups like a family.
4. Closure under post-processing
Differential privacy is invulnerable to post-processing. This means that a data professional is unable to, with a private algorithm, execute a function of the output unless they have extra information about private databases that makes it less differentially private.
Benefits of Differential Privacy
Differential privacy offers many benefits over other privacy measures.
- There is an essential guarantee with differential privacy that those who see the results of a differentially private analysis will generally make the same inferences about any individual’s private information- even if that person’s personal information isn’t included in the analysis’s input.
- Differential privacy mathematically ensures privacy protection and can counter a large variety of privacy attacks, including linkage attacks and differencing attacks.
- A key benefit to differential privacy is the protection it offers from data attacks. The same way that different researchers will receive a different answer for the same query means that, during a data attack, information specific to individual participants won’t be revealed. Differential privacy also uses random noise, which allows any one person in the data set to deny their specific information (or even their participation) with plausibility.
Differential Privacy with Encora
Companies ready to take charge of privacy with the differential privacy model can reach out to Encora for support. Encora’s teams of skilled and experienced software engineers are well-versed in using differential privacy for their many clients. Encora can help companies find all the advantages offered by differential privacy. Companies can take better care of their customers while gaining more use from their data. Please reach out to Encora with questions or to get started.
