Efficient Bilateral Privacy-Preserving Data Collection for Mobile Crowdsensing

Axin Wu, Weiqi Luo, Anjia Yang, Yinghui Zhang, Jianhao Zhu

Abstract: Mobile crowdsensing (MCS) utilizes ubiquitous mobile devices to collect massive amounts of data and offer various high-quality services. During the data collection and upload process, bilateral access control is implemented to recruit qualified data providers and prevent unauthorized access to collected data. However, the efficiency of existing bilateral access control schemes applicable in the data collection phase is dissatisfactory, as their ciphertext sizes are linear with the number of attributes. Additionally, data confidentiality and authenticity, as well as lightweight encryption and decryption processes, are crucial for the deployment of MCS since the former eliminate the risks of data abuse and false data injection, and the latter are typically limited in their computation and communication resources. To reduce the resource consumption of these devices, we present EBAC-CC, an efficient bilateral access control with constant-size ciphertexts that ensures data confidentiality and authenticity and allows for flexible threshold bilateral access control. Besides, offline/online techniques and outsourced decryption are employed to quickly generate ciphertexts and recover perceptual data, which also alleviates their computation burdens. We also prove its privacy and authenticity in the standard model and evaluate its efficacy theoretically and experimentally, demonstrating its superiority over other bilateral access control schemes.

External IDs:doi:10.1109/tsc.2023.3308261