"Algorithms Improve How We Protect Our Data"
Scientists at the Daegu Gyeongbuk Institute of Science and Technology (DGIST) in Korea have developed algorithms to more efficiently measure how difficult it would be for an attacker to guess cryptographic systems' secret keys. Their approach could make it less computationally complex to validate encryption security. Random numbers are imperative for generating cryptographic information. Randomness is vital for securing cryptographic systems. Scientists often use min-entropy, a metric that helps estimate and validate how well a source generates random numbers used to encrypt data. Data with low entropy is easier to decipher, while data with high entropy is significantly harder to decode. However, the min-entropy for some types of sources is difficult to estimate accurately, resulting in underestimations. The DGIST scientists developed an offline algorithm that estimates min-entropy based on a whole data set. They also developed an online estimator that only requires limited data samples. Evaluations have shown that their algorithms can estimate min-entropy 500 times faster than the current standard algorithm while also preserving the accuracy of estimations. This article continues to discuss the importance of randomness for the security of cryptographic systems, the concept of min-entropy, and the algorithms developed by DGIST scientists to better estimate the security level of encrypted data.