Global attestation of location in mobile devices
Saritha Arunkumar, Mudhakar Srivatsa, et al.
MILCOM 2015
Privacy becomes one of the important issues in data-driven applications. The advent of non-PC devices such as Internet-of-Things (IoT) devices for data-driven applications leads to needs for light-weight data anonymization. In this paper, we develop an anonymization framework that expedites model learning in parallel and generates deployable models for devices with low computing capability. We evaluate our framework with various settings such as different data schema and characteristics. Our results exhibit that our framework learns anonymization models up to 16 times faster than a sequential anonymization approach and that it preserves enough information in anonymized data for data-driven applications.
Saritha Arunkumar, Mudhakar Srivatsa, et al.
MILCOM 2015
Tianwei Xing, Sandeep Singh Sandha, et al.
EdgeSys 2018
Shouling Ji, Weiqing Li, et al.
IEEE/ACM TON
Liang Ma, Mudhakar Srivatsa, et al.
ICNP 2016