What machine learning or AI techniques can be used to detect and mask sensitive information in a semi-structured or unstructured dataset.
Younas Khan
A classifier is a taught and validated model that recognises patterns using machine learning methods. This classifier is used to produce predictions for data/objects that have not yet been observed.
Unsupervised learning (UL), on the other hand, is a machine learning technique that works with datasets that do not include labelled answers. It is most typically employed in cluster analysis to discover hidden patterns in huge unlabeled datasets.
The Univariate approach is a machine learning methodology for spotting outliers in data. Explanation: The Univariate approach is one that aids in data analysis in easy steps.
Qamar Ul Islam dear sir, by sensitive information I meant information that is meant to be private and does not necessarily contribute much towards the learning of the model or training, however, in some cases it might contribute. The objective is to detect such information (without human intervention) and encrypt it using some cryptographic algorithm.
Dear Dr Younas Khan . See the following useful RG link: Article A survey on security and privacy of federated learning
Kindly check also the following useful RG link: Chapter Protecting Information with Cybersecurity
Also check please the following very good link: https://harvest.usask.ca/bitstream/handle/10388/8401/MA-THESIS-2018.pdf?sequence=1&isAllowed=y
In relation to your query some other considerable items : https://www.nature.com/articles/s41586-021-03583-3https://arxiv.org/pdf/2102.13376.pdf
https://chakacamps.com/decentralized-reinforcement-learning-applied-to-mobile-robots-pdf
In terms of sensitive data, Federated learning can be a good option. To increase more security, differential privacy can also be employed to mask the federated model. A good example can be as follows.
Article FBI: A Federated Learning-Based Blockchain-Embedded Data Acc...
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