Enabling Secure and Fast Indexing for Privacy-Assured Healthcare Monitoring via Compressive Sensing

Abstract

As e-health technology continues to advance, health related multimedia data is being exponentially generated from healthcare monitoring devices and sensors. Coming with it are the challenges on how to efficiently acquire, index, and process such a huge amount of data for effective healthcare and related decision making, while respecting user's data privacy. In this paper, we propose a secure cloud-based framework for privacy-aware healthcare monitoring systems, which allows fast data acquisition and indexing with strong privacy assurance. For efficient data acquisition, we adopt compressive sensing for easy data sampling, compression, and recovery. We then focus on how to secure and fast index the resulting large amount of continuously generated compressed samples, with the goal to achieve secure selected retrieval over compressed storage. Among others, one particular challenge is the practical demand to cope with the incoming data samples in high acquisition rates. For that problem, we carefully exploit recent efforts on encrypted search, efficient content-based indexing techniques, and fine-grained locking algorithms, to design a novel encrypted index with high-performance customization. It achieves memory efficiency, provable security, as well as greatly improved building speed with nontrivial multithread support. Comprehensive evaluations on Amazon Cloud show that our encrypted design can securely index 1 billion compressed data samples within only 12 min, achieving a throughput of indexing almost 1.4 million encrypted samples per second. Accuracy and visual evaluation on a real healthcare dataset shows good quality of high-value retrieval and recovery over encrypted data samples.