The increasing popularity of smartphones and location-based service (LBS) has brought us a new experience of mobile crowdsourcing marked by the characteristics of network-interconnection and information-sharing. However, these mobile crowdsourcing applications suffer from various inferential attacks based on mobile behavioral factors, such as location semantic, spatiotemporal correlation, etc. Unfortunately, most of the existing techniques protect the participant’s location-privacy according to actual trajectories. Once the protection fails, data leakage will directly threaten the participant’s location-related private information. It open the issue of participating in mobile crowdsourcing service without actual locations. In this paper, we propose a mobility-aware trajectory-prediction solution, TMarkov, for achieving privacy-preserving mobile crowdsourcing. Specifically, we introduce a time-partitioning concept into the Markov model to overcome its traditional limitations. A new transfer model is constructed to record the mobile user’s time-varying behavioral patterns. Then, an unbiased estimation is conducted according to Gibbs Sampling method, because of the data incompleteness. Finally, we have the TMarkov model which characterizes the participant’s dynamic mobile behaviors. With TMarkov in place, a mobility-aware spatiotemporal trajectory is predicted for the mobile user to participate in the crowdsourcing application. Extensive experiments with real-world dataset demonstrate that TMarkov well balances the trade-off between privacy preservation and data usability.
Network measurements are the foundation for network applications. The metrics generated by those measurements help applications improve their performance of the monitored network and harden their security. As severe network attacks using leaked information from a public cloud exist, it raises privacy and security concerns if directly deployed in network measurement services in a third-party public cloud infrastructure. Recent studies, most notably OblivSketch, demonstrated the feasibility of alleviating those concerns by using trusted hardware and Oblivious RAM (ORAM). As their performance is not good enough, and there are certain limitations, they are not suitable for broad deployment. In this paper, we propose FO-Sketch, a more efficient and general network measurement service that meets the most stringent security requirements, especially for a large-scale network with heavy traffic volume and burst traffic. Let a mergeable sketch update the local flow statistics in each local switch; FO-Sketch merges (in an Intel SGX-created enclave) these sketches obliviously to form a global “one big sketch” in the cloud. With the help of Oblivious Shuffle, Divide and Conquer, and SIMD speedup, we optimize all of the critical routines in our FO-Sketch to make it 17.3x faster than a trivial oblivious solution. While keeping the same level of accuracy and packet processing throughput as non-oblivious Elastic Sketch, our FO-Sketch needs only ∼ 4.5MB enclave memory space in total to record metrics and for PORAM to store the global sketch in the cloud. Extensive experiments demonstrate that, for the recommended setting, it takes only ∼ 0.6s in total to rebuild those data during each measurement interval.
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