Mobile crowd-sensing can learn the aggregate statistics over personal data to produce useful knowledge about the world. Since personal data may be privacy-sensitive, the aggregator should only gain desired statistics without learning anything about the personal data. To guarantee differential privacy of personal data under an untrusted aggregator, existing approaches encrypt the noisy personal data, and allow the aggregator to get a noisy sum. However, these approaches lack of either efficient support of dynamic joins and leaves, or secure data-integrity verification, or fault tolerance. In this paper, we propose a novel private data aggregation scheme to address these issues for mobile crowd-sensing applications. In our scheme, we first design an efficient group management protocol to deal with the participants' dynamic joins and leaves. Then we enhance the scheme with data-integrity verification by considering the security vulnerability of limited data range. Moreover, we guarantee fault tolerance by leveraging a future message buffering mechanism, enabling continuously obtaining aggregate results and integrity verifications when failures happen. The analysis indicates that our scheme achieves desired properties, and the performance evaluation demonstrates the scheme's efficiency in terms of communication and computation overhead.
['Jianwei Chen', 'Huadong Ma', 'Dong Zhao']
Private data aggregation with integrity assurance and fault tolerance for mobile crowd-sensing