Privacy has gained interest recently due to the huge amount of data collected and shared through social media platforms, government institutions, and private companies. This raises huge concerns, especially when data is shared with third parties. Sensitive data, such as employee information, strategic plans, contracts, etc., can be exposed if the third-party controls are ineffective or compromised. Another concern is related to the fact that third parties might retain the information for a longer time than required or misuse the information for a purpose other than the original agreement. Also, the data can be corrupted or altered by a third party during the processing. To address these challenges, Homomorphic encryption (HE) is one of the most optimal techniques to protect sensitive data while processing or sharing it with third parties. This paper proposes a comparative study of different homomorphic encryption schemes, including Paillier, Exponential ElGamal, and Okamoto-Uchiyama, to showcase their impact on preserving the privacy of shared data. As a result, Okamoto-Uchiyama outperforms the benchmarks in terms of execution time for encryption, decryption, and addition operations. However, Exponential ElGamal demonstrated efficient encryption and additional operations, but has the slowest decryption process.