As technology advances, more and more businesses are collecting and storing data about their customers. While this data can be helpful for marketing purposes, it also raises privacy concerns. Customers don’t want their personal information accessed or used without consent. The global spending on security products amounted to $125.2 billion in 2020. It shows that businesses are ready to invest in data security.
Privacy enhancing computation (PEC) like Tripleblind solves this problem using data science to protect sensitive datasets. If you want to know more and how it can benefit your company, here is the information.
What Is Privacy-Enhancing Computation?
It is a new area of computer science concerned with ensuring data privacy while it is being processed. It includes developing algorithms and systems that allow for the secure and efficient processing of sensitive information without revealing any data. For example, around 80 percent of social media users are concerned about advertisers and businesses accessing the data they share on social media platforms. Using PEC, companies could still gain access to the information they need without violating users’ privacy. In other words, it can help protect user data while allowing businesses to use it.
What Are the Technologies Used in PEC?
In 2021, approximately 55 percent of online users who experienced data encryption issues reported that unencrypted cloud services are a problem. To mitigate this, various levels and types of encryption can be employed.
Homomorphic encryption is a type of cryptography that allows for mathematical operations to be performed on ciphertext without needing to decrypt it first. It means that data can be processed and examined while maintaining its privacy. Homomorphic encryption schemes are often used in cloud computing, where sensitive information is stored and processed remotely.
There are a few types of homomorphic encryption, but the most common is fully homomorphic encryption (FHE). FHE allows any computation on encrypted data without needing to decrypt it first. It makes it very powerful and helpful for a variety of applications.
Another type is homomorphic encryption with partial decryption (HEPD), which allows for some computation on encrypted data without decrypting it. It can be used, for example, to search through encrypted databases.
The last type is homomorphic encryption with selective decryption (HESD), which allows for specific parts of encrypted data to be decrypted and processed while the rest remains encrypted. HESD can be used, for example, to process payments on a blockchain.
Thus, homomorphic encryption is a powerful tool that can keep data private while still allowing it to be processed and examined.
Federated Learning is a technique for training machine learning models on data distributed across multiple devices without sharing the underlying data. This approach can be used to train models on sensitive data while keeping the data private.
Federated Learning has been used for various tasks, including natural language processing, computer vision, and recommender systems.
Synthetic data is one of the most successful approaches to building differentially private models. Synthetic data is generated data that shares many statistical properties with real-world data but contains no actual information about individuals.
It makes it possible to train models on synthetic data that accurately represent real-world data without access. It allows companies to get all the benefits of using machine learning without sacrificing the privacy of their customers or employees.
Whether or not you think your company will need to use privacy-enhancing computation techniques like Tripleblind in the future, it is important to understand how it works. This technology can revolutionize many industries and change how you think about data privacy.