Data privacy is a major concern as the globe enters the new era of big data. While it is convenient for customers to shop, manage their finances, and include personal information on online forms, contests, and other promotions or chores, it is not without risk. Reputable businesses store this data and use it to fine tune their business plans. However, there are security risks associated with the sharing and use of private information.
In this void various privacy-preserving approaches emerged that allow enterprises to perform big data analysis such as statistical estimation, statistical learning, and data mining. One such approach is Differential Privacy.
In this article, you will learn the essentials of differential privacy including:
What is Differential Privacy?
Differential privacy is a methodology for sharing information without compromising any personal data about an individual. It incorporates technology that conceals any personal or identifying PII information about individual in a dataset while still allowing analysts and researchers access to the dataset.
Differential privacy has several steps and relies on a privacy guard. This software serves as a gatekeeper of the secure information.
- Any query or request for information is evaluated for its privacy impact using an algorithm.
- The privacy guard passes the request to the database.
- The database returns a clean unmasked response.
- The data guard then distorts this response with noise scaled to optimal level to disguise any personal information.
- The noisy modified data is returned to the anyst or individual who requested the information.
The level of noise or distraction must be significant enough to safeguard privacy while being constrained sufficiently to maintain the value of the information provided to analysts.
In its simplest form, differential privacy creates data anonymously by adding random noise to the dataset. It enables data analysts to carry out every statistical analysis without identifying individuals or their corresponding sensitive data. The differential privacy LaPlace mechanism is one of the most commonly used differential privacy statistics.
The Differential Privacy LaPlace Distribution
The LaPlace Distribution, commonly used in numerical data applications, is the workhorse of differentially private mechanisms. Its advantage over other processes, such as the exponential mechanism, is that it is mathematically and computationally simpler.
Private Data Analysis Methods
Aside from the LaPlace mechanism, there are several other methods for ensuring data privacy, we compare these methods to differential privacy.
Differential Privacy vs. K Anonymity
As already stated, differential privacy preserves individual privacy by obscuring the underlying value of the data by adding noise, thereby making it private. By doing this, businesses can conceal PII without significantly affecting the usefulness of the data. This concealment indicates that since the dataset contains the features of a whole population, the statistical conclusions drawn from are not influenced by an individual’s contribution.
On the other hand, the K Anonymity definition states that it is a privacy model typically used to protect the subject’s confidentiality in situations involving the exchange of information by anonymizing data. This model suppresses or generalizes attributes until every row is the same as at least (K-1) other rows. At this point the database is considered to be K Anonymous.
The ultimate goal of many differentially private algorithms is to maintain individual anonymity. On the surface, anonymity refers to being unidentifiable. But if you look at it closely, you will see that genuine anonymization cannot be achieved by just removing names from a dataset.
Combining anonymized data with another dataset makes it possible to re-identify the original data. The data may contain details that, while not unique identifiers, can be recognized when connected to other databases.
In this situation, K Anonymity hinders concrete database links. It manages data by requiring that at least K persons have the same combination of quasi-identifiers values, which are characteristics that infer someone’s identification indirectly. In the worst-case scenario, the data reduces an individual entry to a group of K individuals.
Differential Privacy vs. Homomorphic Encryption
The drawback of encrypted data is that it requires decryption to be usable. This method opens it to the precise threats you intend the encryption to defeat.Homomorphic encryption offers a potent remedy for this situation.
Homomorphic encryption allows for the analysis or manipulation of encrypted data without disclosing the information to anyone.
For example when you search for a local restaurant, the search returns a significant amount ofinformation to third parties, including the fact that you are looking for a restaurant, where you are searching, the current time, and more. Homomorphic encryption would hide this information, and the response you received regarding the location and directions to the restaurant from Google and other service providers.
Patient privacy is crucial in industries that deal with sensitive personal data, such as financial services or healthcare. In these situations, homomorphic encryption can safeguard the private information of the actual data while allowing for analysis and processing.
Homomorphic encryption encrypts the data using a public key, just like other types of encryption. However, it employs an algebraic framework, in contrast to different kinds of encryption, to enable functions to be applied to the data while it is still encrypted.
After the functions and manipulation get completed, only the person with the matching private key can view the unencrypted data. This option allows the data to be secure and confidential while still being used.
Differential privacy, on the other hand, protects an individual’s privacy by introducing some random noise into the dataset while carrying out the data analysis. Simply put, adding noise makes it impossible to distinguish between different pieces of information based on the results of an analysis.
However, adding noise changes the outcome of the analysis into an approximation rather than the exact value that could have only been attained using the real dataset. Since, the noise is randomly created by software, this noise can be different every time the request for information is made, even if the request is the same. Therefore, it is also very likely that if a private differential analysis is run numerous times, it might provide different results each time.
Ultimately, you can implement differential privacy depending on your needed trade-off between privacy and accuracy.
Examples Of Differential Privacy
Differential privacy is a mathematical strategy that prevents anyone from learning information about the individuals in a dataset by introducing a predetermined amount of random noise to the dataset. Although it can impact the accuracy of datasets, this level of privacy has a real-world necessity for specific industries and use-cases.
Here are a few examples of uses for Differential Privacy:
- Known as Randomized Aggregatable Privacy-Preserving Ordinal Response (RAPPOR), Google’s differential privacy feature was first made available in Chrome in 2014. It aids Google in analyzing and gleaning insights from browser usage while shielding personally identifiable information. In 2019, Google also released its differential privacy libraries as open-source.
- The U.S. Census Bureau began using differentiated privacy with the 2020 Census data. U.S. citizens get covered in great detail in the dataset. Without privacy safeguards, you can easily trace this information back to specific people. The Bureau claims that traditional methods of anonymity have become outmoded. This action is due to re-identification methods allowing data about a particular individual to get extracted from an anonymous dataset. With differential privacy, this issue has gotten addressed.
- For sensitive personal information, including emojis, search searches, and health data, Apple implements differential privacy in its iOS and macOS products.
Differential privacy offers the ability for organizations to analyze and use the wealth of information they have stored while still protecting individual peronsal data and privacy. Differentially private algorithms ensure that cybercriminals can only understand as much about a person as they would if that person’s record were missing from the dataset.
Satori offers an alternative to differential privacy that is easier to apply at scale on all your data platforms. Schedule a demo with one of our experts to learn how Satori helps organizations easily mask PII and keep sensitive data secure across all your data repositories; while still enabling data teams to use the information quickly and easily to fully leverage the information.