Reference:
Homomorphic encryption is a type of encryption that allows computation and analysis of data within a ciphertext without knowledge of the plaintext. This means that encrypted data can be processed without being decrypted first, which enhances the security and privacy of the data. Homomorphic encryption can enable applications such as secure cloud computing, machine learning, and data analytics. Reference: https://www.ibm.com/security/homomorphic-encryption https://www.synopsys.com/blogs/software-security/homomorphic-encryption/

See the Explanation below for the solution
Explanation:
Code Snippet 1
Vulnerability 1: SQL injection
SQL injection is a type of attack that exploits a vulnerability in the code that interacts with a database. An attacker can inject malicious SQL commands into the input fields, such as username or password, and execute them on the database server. This can result in data theft, data corruption, or unauthorized access.
Fix 1: Perform input sanitization of the userid field.
Input sanitization is a technique that prevents SQL injection by validating and filtering the user input values before passing them to the database. The input sanitization should remove any special characters, such as quotes, semicolons, or dashes, that can alter the intended SQL query. Alternatively, the input sanitization can use a whitelist of allowed values and reject any other values.
Code Snippet 2
Vulnerability 2: Cross-site request forgery
Cross-site request forgery (CSRF) is a type of attack that exploits a vulnerability in the code that handles web requests. An attacker can trick a user into sending a malicious web request to a server that performs an action on behalf of the user, such as changing their password, transferring funds, or deleting dat a. This can result in unauthorized actions, data loss, or account compromise.
Fix 2: Implement anti-forgery tokens.
Anti-forgery tokens are techniques that prevent CSRF by adding a unique and secret value to each web request that is generated by the server and verified by the server before performing the action. The anti-forgery token should be different for each user and each session, and should not be predictable or reusable by an attacker. This way, only legitimate web requests from the user's browser can be accepted by the server.

The best way to achieve the objective of discovering SaaS applications and blocking access to unapproved or identified as risky ones is to implement cloud infrastructure to proxy all user web traffic and control access according to centralized policy (C). This solution would allow the security architect to inspect all web traffic and enforce access control policies centrally. This solution also allows the security architect to detect and block risky SaaS applications.

A) Processing data on a server after decrypting in order to prevent unauthorized access in transit is not a common use case for homomorphic encryption, because it does not take advantage of the main feature of homomorphic encryption, which is computing over encrypted data. This use case can be achieved by using any standard encryption method that provides confidentiality for data in transit.
B) Maintaining the confidentiality of data both at rest and in transit to and from a CSP for processing is not a common use case for homomorphic encryption, because it does not take advantage of the main feature of homomorphic encryption, which is computing over encrypted data. This use case can be achieved by using any standard encryption method that provides confidentiality for data at rest and in transit.
D) Storing proprietary data across multiple nodes in a private cloud to prevent access by unauthenticated users is not a common use case for homomorphic encryption, because it does not involve any computation over encrypted data. This use case can be achieved by using any standard encryption method that provides confidentiality for data at rest.
https://www.splunk.com/en_us/blog/learn/homomorphic-encryption.html
https://research.aimultiple.com/homomorphic-encryption/
Explanation:
Homomorphic encryption is a type of encryption method that allows computations to be performed on encrypted data without first decrypting it with a secret key. The results of the computations also remain encrypted and can only be decrypted by the owner of the private key. Homomorphic encryption can be used for privacy-preserving outsourced storage and computation. This means that data can be encrypted and sent to a cloud service provider (CSP) for processing, without revealing any information to the CSP or anyone else who might intercept the data. Homomorphic encryption can enable new services and applications that require processing confidential data on a large number of resources, such as machine learning, data analytics, health care, finance, and voting.