| Description
| The implementation of computation has been completed independently from the local machine, and then outsourced to the cloud (or some service providers) to complete it. However, with the delegation of data or applications, accordingly the security of such computation must be revisited. This course will start from the data confidentiality, discuss how to use homomorphic encryption to easily complete a secure outsourced computation, and then use other lightweight components (garbled circuit, secret sharing) to replace homomorphic encryption and redesign the secure protocols. Then it will move to define the privacy of data, introduce de-identification techniques such as k-anonymity, and finally bring the most rigorous notion of differential privacy. Through the above-mentioned methods of preserving privacy and security, artificial intelligence and machine learning algorithms are introduced into the outsourced computation to reconstruct a new type of the framework for different scenarios.
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