2022 Techniques That Make Privacy-Enhancing Computation Beneficial

Today, keeping your data secure on the internet is extremely vital. There’re are a lot of reasons for that and the prime one is data abuse that can lead to legal penalties and actions on behalf of the victims as well. No one wants to share their data with other people whether we talk on an individual or enterprise scale. No business wants to share its data with others due to privacy reasons and customer information protection policies. But, how does the data remain safe at individual or enterprise levels with so many hackers and cybercriminals lurking on the internet?

One of the most perfect solutions for protecting online data is privacy-enhancing computation. It’s used for extracting valuable information out of datasets without exposing the entirety of the data or identities of data sources. This involves sensitive data and information but without citations and precise sources due to privacy concerns. Several data items like bank account numbers and social security numbers aren’t visible to the public.

2022 Techniques in Privacy-Enhancing Computation

Here’re the top 2022 techniques that have made privacy-enhancing computation beneficial for everyone. Different techniques might involve the use of different types of technologies. This brings us to the discussion that privacy-enhancing computation isn’t summed up by a single technology. Rather, it utilizes multiple technologies and systems to create a solid framework for sharing information without exposing sensitive data. The said techniques are as follows:

· Differential Privacy

It’s a privacy system that enables users to utilize databases and extract valuable information via a hassle-free process. However, it keeps the identity of every individual group member safe and hidden.

· Zero-Knowledge Proofs

Also known as Zero-Knowledge Protocol, it refers to the process of sharing information between two users. One part shares information from a collective dataset but only the true data. While sharing only the factual information, the sending party doesn’t reveal anything else.

· Homomorphic Encryption

In simple words, this form of encryption is one of the latest techniques deployed by scientists and researchers around the world to protect data intended for computing. During the processing and computation, the encrypted data remains that way unless deemed necessary.

· TEE (Trusted Execution Environments)

It’s a specific processer that maintains the integrity and confidentiality of your data and codes. It’s a trusted execution environment where your chosen data remains confidential and helps in protection.

· Multi-Party Computations

This process involves using the data from different parties over your own responsibility without revealing individual datasets. This also involves people functioning together as a team over different inputs. Using shared operations in a computational environment, no party will learn about the other party’s actions for the data during the processing.

Conclusion

On daily basis, huge datasets move from one processing group to another without revealing identities or exposing the privacy of sources. For instance, millions of bank accounts and social media platforms reveal new data every day. However, this doesn’t mean that the collective valuable information from the raw big data needs to disclose the identity of each dataset. People often agree to let their personal information be used in research and scientific analysis. But, all individuals wish to keep their identities anonymous.