Toward Easier Development of Privacy-Preserving Mobile Crowdsensing Applications

Fully Homomorphic Encryption (FHE) schemes allow computations over encrypted data without access to the decryption key. This technique can be a valuable tool for building privacy into crowdsensing systems; however, many existing FHE implementations, such as Microsoft's SEAL, are difficult to implement into mobile applications. This paper presents a natively compiled Dart plugin that abstracts the underlying C/C++ SEAL library. The FHE Library plugin enables developers to access SEAL's full functionality within other Dart plugins and Flutter applications and is extensible to other encryption libraries. To evaluate the versatility of the plugin, we develop a Dart plugin to calculate several distance measures between two sets of encrypted inputs and we develop a Flutter application called GhostPeerShare. The Distance Measure plugin implements Kullback-Leibler Divergence, Bhattacharyya Coefficient, and Cramer Distance. GhostPeerShare demonstrates the use of a plugin by re-implementing Proof of Presence Share (PopShare), a mobile application that privately identifies similar videos recorded by users, as a Flutter application. Through these applications, we demonstrate that performance is similar to native applications and that utilizing FHE is more accessible to researchers developing crowdsensing applications.