Exam CISSP topic 1 question 55 discussion

Should be A. Techniques of Data Anonymization 1. Data masking 2. Pseudonymization 3. Generalization 4. Data swapping 5. Data perturbation 6. Synthetic data Reference : https://corporatefinanceinstitute.com/resources/business-intelligence/data-anonymization/

B is much better

The best approach to anonymizing personally identifiable information (PII) in a test environment is B. Randomizing data. Randomizing data involves altering the original data in such a way that it cannot be traced back to the individual, ensuring privacy and compliance with data protection regulations like GDPR12. This method is effective because it maintains the structure and format of the data while making it impossible to identify the original individuals. Option A, "Swapping data," is incorrect because it involves exchanging data values between different records. While this can obscure the original data, it does not fully anonymize it. Swapped data can sometimes be re-identified if the swapping pattern is discovered or if there are other data points that can be correlated12. Randomizing data, on the other hand, alters the data in a way that makes it impossible to trace back to the original individuals, providing a higher level of anonymity and security

Swapping data may not provide sufficient protection against re-identification if the dataset is small.

What is the BEST approach to anonymizing personally identifiable information (PII) in a test environment? A. Swapping data B. Randomizing data C. Encoding data D. Encrypting data The BEST approach to anonymizing personally identifiable information (PII) in a test environment is: B. Randomizing data Here's why: Randomizing data effectively removes the association between the data and the individuals it represents, making it difficult to re-identify the individuals from the anonymized dataset. This approach ensures that the data cannot be traced back to specific individuals, which is crucial for maintaining privacy and confidentiality in a test environment. Swapping data (A) and encoding data (C) may help in anonymization, but they are generally less effective than randomization in ensuring data cannot be traced back. Encrypting data (D) secures the data but doesn't anonymize it, as the original data can still be accessed with the appropriate decryption keys.

OSG states "Randomized masking can be an effective method of anonymizing data" pg 203

I am not sure if an encrepted data casn be used in a test environment. But I will go for option B

A. Swapping data Data swapping involves exchanging values between different records in a dataset, which helps preserve the confidentiality of individual data entries while maintaining the overall statistical distribution and relationships within the data. This technique is more effective than some other common anonymization methods: Randomizing data alters values with random, mock data but doesn't maintain the exact statistical distribution, which can compromise data utility for complex datasets. Data swapping, on the other hand, provides a straightforward way to anonymize PII while preserving data integrity and statistical accuracy appropriate for testing needs. It enables realistic datasets for software development and testing without exposing sensitive information

B. Randomizing data: Randomizing data is a common approach to anonymization. It involves replacing original data values with randomly generated values that do not correspond to any real individuals. This ensures that the data cannot be traced back to its original source while still maintaining its structural and statistical properties for testing.

Only option B refers to anonymization. A. Swapping Data- Pseudonymization B. Randomizing Data- Anonymization C. Encoding Data- Tokenization D. Encrypting Data- Tokenization

Answer: B. Randomizing data A test environment might including needing to test analytics, computations, reports, dashboards - basically processes that have to be tested with unencrypted data. Randomizing data involves replacing PII with randomly generated values while maintaining the statistical properties of the original data. This ensures that the computations and analytics performed on the anonymized data yield accurate results and reflect the real-world scenarios. Swapping data involves replacing PII with other data. Swapping data may introduce biases or alter the statistical properties needed for accurate analytics and computations. Encoding data transforms data into a different representation using encoding schemes. Encrypting data is not be the best choice for this scenario as encryption aims to protect data rather than anonymize it.

Randomized masking can be an effective method of anonymizing data pg279 cissp study guide

Answer B) Encrypting the data does not remove Pii...it just prevents anyone that stole the data from reading it without the proper credentials. However, someone with proper rights...like a DBA can see the Pii data without a problem. Also, Page 202-204 in CISSP study guide clear states randomization as the best option to anonymize data in a way that it will even make GDPR a non-issue.

A is my answer

The correct Answer is A. There are also some well-known techniques to be applied in a structured database for anonymization: Masking: removing, encrypting, or obscuring the private identifiers Pseudonymization: Replace the private identifiers with pseudonyms or false values Generalization: Replacing a specific identifier value with a more general one Swapping: Shuffling the attribute values of the dataset so that they are different from the original one Perturbation: Changing the data by introducing random noises or using random methods

It is randomization. Even if you remove not only encrypt personal data, in some cases it is still not anonymized. The ideal answer would be "Randomized masking".

The encryption effect is not very good, isn't it? Unlike the strict protection of data in production environments, application developers can access test environment data, so it is easy to decrypt data