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Solving Location Privacy For COVID-19 Contact Tracing With Homomorphic Encryption
The next few years will see the rise of homomorphic encryption and lattice encryption, and especially in protecting sensitive information. With partial homomorphic encryption (PHE) we can perform a few operations, such as with Pallier and where we can add two ciphered values [here]. But with full homomorphic encryption (FHE) we can perform add, subtract, multiply and divide. In this way, we can operate on encrypted values with our arithmetic operators, and then decrypt them to value the result of the operation.
One of the best methods around is HEAAN (Homomorphic Encryption for Arithmetic of Approximate Numbers) and which defines a homomorphic encryption (HE) library proposed by Cheon, Kim, Kim and Song (CKKS). CKKS uses approximate arithmetics over complex numbers, and where we take inputs of a and b and then encrypt them, and then subtract the encrypted values, and finally a result of a−b. A sample run is [here]:
HEAAN parameters : logN = 10, logQ = 30, levels = 8 (270 bits), logPrecision = 13, logScale = 30, sigma = 3.190000 Input: 3.200000-7.300000
Cipher: &{[0xc00012b500 0xc00012b520] 0xc0000962c0 30 0xc00012b540 true false}Decrypted: -4.10
