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Description
Ensuring the confidentiality and protection of personal information in big data is an important aspect in data processing. One of the effective methods to achieve a high level of protection is depersonalization of data. The article presents an overview of modern methods of preserving personal data when conducting various kinds of research, in business analytics, etc. The influence of quasi-identifiers on the probability of re-identification is estimated. To reduce the probability of data de-identification, a hashing method based on the use of the Kessak-256 hash function and the addition of a dynamic random string for each dataset element is proposed. This method allows you to significantly increase the time of hacking and the amount of resources required by the attacker. This approach can be used for secure data transmission, exchange and storage.
