Location-based services (LBS) require a user’s coordinates to perform calculations. This introduces privacy risks as data needs to be decrypted in order for computations to be performed. This study explores homomorphic encryption (HE) as a method to enable these computations without the need for decryption. In this study, we develop an application with the Node-seal library to perform homomorphic encryptions and computations. Due to HE’s mathematical constraints, a Euclidean formula is used instead of more precise models such as Karney’s formula. We then perform a quantitative study on a large dataset of randomized coordinates where encrypted computation time and precision is compared against plaintext computation time, and distance computations are compared against Karney’s formula. The results show that computation time increases significantly for encrypted computations, but the precision and relative deviation from Karney’s formula were found to be within acceptable bounds for certain LBS scenarios. Future work may explore alternative HE libraries and support for more complex geodetic distance formulas.