Companies today are leveraging more and more user data to create models that improve their products and user experience. Companies are looking to customers’ needs, wants, and demands to develop products as per their requirements. However, this predictive capability using data is often harmful to individuals who wish to guard their privacy.
Now, there are many technologies to realize the security and privacy of sensitive data in AI models. one of the highest technology to realize security and privacy in AI are Differential Privacy. Differential privacy aims to grant individuals included during a database an equivalent degree of privacy as if their data was completely overlooked. The system is differentially private when the info is structured in such how that you simply cannot tell whether a specific subject participated or not.
Then the technology that’s widely used is Secure Multi-Party Computation. Secure multiparty computation (MPC / SMPC) may be a cryptographic protocol that distributes computation across multiple parties where no individual party can see the opposite parties’ data. Secure multiparty computation protocols can enable data scientists and analysts to compliantly, securely, and privately compute on distributed data without ever exposing or moving it.
The other technology is federated learning. Federated learning (also referred to as collaborative learning) may be a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. This approach stands in contrast to traditional centralized machine learning techniques where all the local datasets are uploaded to at least one server, also on more classical decentralized approaches which frequently assume that local data samples are identically distributed.
Federated learning enables multiple actors to create a standard, robust machine learning model without sharing data, thus allowing them to deal with critical issues like data privacy, data security, data access rights, and access to heterogeneous data.
Homomorphic Encryption is that another method wont to achieve security and privacy of knowledge. Homomorphic encryption may be a sort of encryption allowing one to perform calculations on encrypted data without decrypting it first. The results of the computation are in an encrypted form, when decrypted the output is that the same as if the operations had been performed on the unencrypted data.
Homomorphic encryption is often used for privacy-preserving outsourced storage and computation. this enables data to be encrypted and out-sourced to commercial cloud environments for processing, all while encrypted. In highly regulated industries, like health care, homomorphic encryption is often wont to enable new services by removing privacy barriers inhibiting data sharing. Blockchain is the other method.
Blockchain can encrypt and store the hashcode of data in separate time stamped block headers. At the time of processing data, the integrity of knowledge is often verified and matched with any changes made in previous blocks. Through verifiable tracking of raw and processed datasets, blockchain can maintain optimum characteristics of the AI model. These are the techniques that are increasingly utilized by companies to preserve privacy and security of knowledge
