A Privacy Impact Assessment (PIA) is an organized and formal approach to evaluating the risks to privacy that are likely to arise from a new […]
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Privacy by Design ISPPS- Design from very beginning
Privacy by Design (PbD) Proactive approach to privacy and PbD assures that privacy is present in the target design. Such a shift in the paradigm […]
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Digitization enables Tokenization
Tokenization is a security process that replaces sensitive data with unique identifiers, or tokens to help reduce the chances of data breaches and unauthorized access. […]
Lesson 4 — Data Masking Hiding Sensitive Data
The answer above refers to Data Masking, which is a technique that allows to protect sensitive data but have an unreadable data that resembles the […]
Pseudonymization A Partial Privacy Solution
When personal identifiers are replaced by pseudonyms (artificial identifiers) we refer to the pseudonymization process. This does not remove all personal data but removes most […]
Forget — A Digital Age Privacy Protection
Anonymization is the approach of cleaning or masking identifiers where addresses can not be known on individual-level to data records. In an era where data […]
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Ensuring Privacy-Preserving Machine Learning Safeguarding Data while Enabling Insights
Machine learning is an essential aspect of data science nowadays among all the other options we have to extract any valuable insights out of huge […]
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SMPC{—}Collaborating without Sharing Secrets
Secure Multi-Party Computation(SMPC) is a cryptographic method suitable to allow several parties to jointly compute a function of their private inputs without the need to […]
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Federated Learning — Training Together Without The Data
What is Federated Learning current_medical_grade Federated learning is a machinelearning technique that trains a model across multiple decentralized devices or servers holding local data samples, […]
Designed for DiffPriv Individual Privacy in the Big Team@Data
Every organization wants to capture the best possible insight from the data they collect and this is especially true for the contextual and personal data. […]