Ingenious Ways of Encryption that Ensure Privacy of Big Data

Fool-proof encryption is a basic human right and essential for secure data exchanges.

In a recent report Meta, formerly known as Facebook, stated that it is rolling out default end-to-end encryption in 2023. This is precise because Meta, Businesses for Social Responsibility, a non-profit organization considers encryption as a basic human right and essential for secure data exchanges. However, encryption algorithms become weak over time by developing security holes, putting data at risk. Encryption techniques are essentially used to protect unauthorised access to the privy data by third parties. To prevent eavesdropping, the data is overlaid by an encryption key, so that the data is not the same data for people who do not have access to the key. The standard algorithms include the Secure Hash Algorithm (SHA), and Advanced Encryption Standard (AES) algorithm, which protect data and are capable of withstanding more advanced cyber-attacks. However, in view of emerging technologies like block-chain, quantum computing, and cloud computing, the conventional protocols designed fall inadequate for ensuring data privacy. Meanwhile, the researchers are finding ways to bypass the advanced code-cracking technologies to ensure that the big data remains within the confines of safety. Here are some examples:

Quantum-proof Encryption:

Quantum computers use qubits unlike the 0s and 1s of conventional computing. Therefore, the computing power is way superior to the normal computers, including in performing mathematical problems that underpin the modern encryption algorithms. "Researchers have known for decades that if a large-scale quantum computer could be built, it could do some pretty big calculations that would threaten the cryptosystems that we rely on today for security," says Dustin Moody, a mathematician at NIST, the US National Institute of Standards and Technology. The quantum-proof encryption uses lattice-based cryptography with enormous grids and billions of individual points across thousands of dimensions. Breaking the code would require moving along a set of random points. Unless you know the route, it is highly impossible to break the code.

Homomorphic Encryption:

In order to see original data, encrypted data should be decrypted and this very process can make it vulnerable to breach. Homomorphic encryption has a solution to overcome these encryption holes. It basically involves masking the data with algebraic functions for data manipulation which lets the data remain encrypted while being used. The person on the other end has to use a private key along with the public key in order to access the data. Homomorphic encryption is particularly functional in cases of protecting personal data without having third parties like Google or companies who do not have a direct relationship with the data, involve in the transaction. This encryption is particularly useful in healthcare and defence industries, where personal data is of utmost precious.

Differential Privacy:

Unlike end-to-end encryption differential, privacy encryption uses mathematical noise to mask the original calculations used in the algorithm. The noise terms are large enough for individual variables but small enough for the pattern to be revealed. Craig Gentry, an American computer scientist describes homomorphic encryption as a glovebox. Anybody can put their hands into it and manipulate it but cannot make the final product out. Only the person with the key can take the finished product, once the product is ready. American Census Bureau is actively applying this technique to protect its citizens' data while making it available for lawmakers to plan for policies.

Block-chain Cryptography:

Cryptography for blockchain technology is definitely the hottest area, where many players are putting their currency in. Given the openness and the stakes it holds in terms of monetary value, blockchain is one of the most vulnerable technologies. Till recently block-chain technologies used protocols based on digital signatures to authenticate transactions. These protocols require one key to sign in for all the transactions pertaining to an account. Of late, protocols like ZK-Snark, an example of a zero-knowledge proof protocol, are being applied which enable to confirm of a transaction without having to reveal the identity. ZoKrates, a toolbox used for implementing ZK-Snark protocol on Ethereum helps the user with verifiable computation on DApp. While the earlier protocols only helped with user identification, the latest ones add layers of software to track the entire course of transactions.

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