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6.0
Scalable and Composable Privacy-
Preserving Analytics
Studies show that anonymizing data for analytics is insufficient for ensuring user privacy.
Below are the best techniques to ensure privacy in a big data environment.
6.1 Implement differential privacy
6.1.1 Why?
To protect privacy even when data is linked with external data sources. Anonymizing
public records have failed when researchers manage to identify personal information by
linking two or more separately innocuous databases
6.1.2 How?
Differential privacy [Dwo06] aims to provide a means to maximize the accuracy of
queries from statistical databases while minimizing the chances of identifying its
records. Differential privacy is the mathematical concept to measure how much (or how
little) anonymity is preserved on a database. For example, adding random noise is a
method to achieve some level of differential privacy. Users are encouraged to use the
appropriate mechanism for a given use.
6.2 Implement Utilize homomorphic encryption
6.2.1 Why?
To enable encrypted data to be stored and processed on the cloud. Data stored in
plaintext on the cloud may be compromised and cause privacy risks. On the other hand,
when only encrypted data is stored on the cloud, utility of data is significantly limited.
6.2.2 How?
Homomorphic encryption is a form of encryption that allows specific types of
computations to be carried out on ciphertext. The method allows users to obtain an
encrypted result that, when decrypted, matches the result of operations performed on
6.1.1 Why?
CLOUD SECURITY ALLIANCE Big Data Working Group Guidance
© Copyright 2016, Cloud Security Alliance. All rights reserved.
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