¿Cuáles son algunas de las mejores prácticas para la anonimización y el cifrado de datos?

Best practices for data anonymization and encryption start with understanding what data anonymization is. Data anonymization involves transforming personal data into a format that cannot be traced back to an individual. Techniques include removing or masking personally identifiable information (PII) and using generalization or perturbation methods.

Data anonymization is about changing or removing personal details from datasets, so the people the data is about stay unknown. Understanding and implementing best practices for data anonymization and encryption is crucial in today's digital landscape to safeguard sensitive information while upholding privacy standards. Your interest in this topic demonstrates a clear commitment to enhancing data security, which is essential across all industries.

Data anonymization is the process of transforming data in such a way that individuals cannot be identified from the information provided. This technique is used to protect the privacy and confidentiality of individuals while still allowing the data to be used for analysis and research purposes. During data anonymization, personally identifiable information (PII) such as names, addresses, social security numbers, and other identifying details are removed or obfuscated to prevent the identification of specific individuals. Techniques such as masking, generalization, and data perturbation are commonly employed to anonymize data while preserving its utility for analysis.

As per me Data Anonymization is one of the process in which personal data or personal identification is altered in such a way that a data subject can no longer be identified directly or indirectly, either by the data controller alone or in collaboration with any other party.

K-Anonymization techniques are used for data anonymization. In K-Anonymization technique, data fields are divided into identifier, non-identifier and quasi-identifier(somewhat identifier). For example, the name is identifier field. Age, gender & religions are quasi-identifier field. In this algorithm, we can either generalize or suppress the field. In data suppression, information will be replaced by "*". In data generalization, value would be replaced by the broader value. For example, 9 can be replaced by 5<x<=10. In K-Anonymization, k is selected such that, the attributes are anonymized. A least K rows should be present with the same attributes after processing. E.g. if K = 2, then there will be 2 rows with the same set of attributes.

Data encryption is one of the techniques used to secure and transform the data. This can be used when the data is being hosted and stored in the cloud, where the data is exposed and has the threat of getting leaked or hacked. Converting the data to an unreadable format is better since outsiders cannot understand the data and cannot manipulate it.

Data anonymization is critically important for our company work as we work on programms evaluations 1- in some evaluations we meet children. these children views should be carefully collected according to certain protocol. their parents should agree on the process. so we do anonymization to protect them and their views. 2- in some evaluations we also meet stakeholders Representatives. those people trust us with their experiences and views. some of these views are valuable but also may put them in trouble especially if their superiors asked the source. so those people views should be protected we always put data anonymous, present the results on the aggregate levels. offer recommendations rather than weakness points.

When dealing with highly sensitive data such as health records it may be useful to use compute to data libraries to create synthetic data

Encrypting data in RDBM means take a colums that contains some data and transform it into unreadeable format. For data that are strings values this means using an encryption algoritm one transform a value into a new one by using some encryption algoritm. Keep in mind that this transform could alter the size of the string so a good way to handle this is to create a new database starting from the original one. So for example a varchar(20) field that could handle first name when using encryption can become a varchar(33) when using MD5 encryption. so prior to apply encryption one need to design the new values. What works for string works the same way for numbers, but does not work so well for datetime and timestamp field.

When creating a DB in general also from the point of view for encryption, it is strongly suggest that in every table the unique row ID is mapped with GUID or id also from the relation point of view. If we choose for example a username in user table to be the ROW ID when then we need to encrypt or mask it you are at risk of loosing the relation with other table. While if you identify the user with an ID that has nothing to do with the other data that the external relation with other tables are manteined even if you encrypt other columns. Back to basis a good study prior to create the DB is mandatory non only to structure query in the correct way but also ensure to avoid further issues in terms of encryption.

Build in regular reviews/audits into standard operating procedures. - are all elements, from staff training and access, to continued compliance - what are the risks of re-identification? have they been mitigated? - how are encryption keys managed? - are we transmitting any unencrypted data?