Privacy enhancing computation is where a computer, or algorithm, is designed to ensure a person’s privacy. It uses information about the person to protect them. Computation techniques may be used to protect the privacy of someone by anonymizing their information, masking their IP address, or protecting their identifiers.

One of the most exciting developments in computer science in the last few decades has been the rise of what is known as “privacy-preserving computation.”

Summing it up in simple words-

In simple terms, the privacy-preserving computation means taking an input, such as a person’s data, and producing an output, which is then acceptable for the intended use. The outputs are the same as the input, and they’re just “improperly” computed.

This has several applications, but two, in particular, are of great interest to data scientists:

• homomorphic encryption
• differential privacy.

If we look into the business field, every work is done online, and whether they are small, medium or large businesses, they will have to protect their data in use.

According to Gartner, by 2025, 50% of large organizations will opt for privacy-enhancing computation as they are dealing in an untrusted environment.

Techniques that are combined to make privacy-enhancing computation are-

1. a) Multi-party computations- This technique aims to let people work together in computing functions without revealing them individually. In this, an individual can not know what others did work during the process.
2. b) Zero-knowledge proofs- this technique is used when an individual shares true information but nothing else is revealed.
3. c) Trusted execution environments (TEE)- a secure area where codes and data are protected with full confidentiality in the main processor.
4. d) Homomorphic encryptions- Homomorphic encryption is a subfield of cryptography that enables secure computations on encrypted data.

Disadvantages of privacy-enhancing computation-

1. a) Can be very complex and hard to implement and manage.
2. b) Can be expensive as they need large computational capacity
3. c) It can also be harmful to the environment as large data can cause major bandwidth problems.

The use of privacy-enhancing computation can be seen in the fields like-

• Medical research
• Human resource
• Internal data analysis
• Fraud prevention

All and all, there are both benefits and disadvantages of using privacy-enhancing computation, but we should make sure whatever it is providing matches our needs. If we need a PEC that provides speed, we should go for that because some PECs are slower than others available. We should not forget that PEC also affects the environment.

Privacy enhancing computation