Re-DPoctor: Real-Time Health Data Releasing with W-Day Differential Privacy

Wearable devices enable users to collect health data and share them with healthcare providers for improved health service. Since health data contain privacy-sensitive information, unprotected data release system may result in privacy leakage problem. Most of the existing work use differential privacy for private data release. However, they have limitations in healthcare scenarios because they do not consider the unique features of health data being collected from wearables, such as continuous real-time collection and pattern preservation. In this paper, we propose Re-DPoctor, a real-time health data releasing scheme with w-day differential privacy where the privacy of health data collected from any consecutive w days is preserved. We improve utility by using a specially-designed partition algorithm to protect the health data patterns. Meanwhile, we improve privacy preservation by applying newly proposed adaptive sampling tech- nique and budget allocation method. We prove that Re-DPoctor satisfies w-day differential privacy. Experiments on real health data demonstrates that our method achieves better utility with strong privacy guarantee than existing state-of-the-art methods.

[1]  Jiming Chen,et al.  Full-View Area Coverage in Camera Sensor Networks: Dimension Reduction and Near-Optimal Solutions , 2016, IEEE Transactions on Vehicular Technology.

[2]  Johannes Gehrke,et al.  iReduct: differential privacy with reduced relative errors , 2011, SIGMOD '11.

[3]  Stavros Papadopoulos,et al.  Practical Differential Privacy via Grouping and Smoothing , 2013, Proc. VLDB Endow..

[4]  Bing-Rong Lin,et al.  Towards an axiomatization of statistical privacy and utility , 2010, PODS.

[5]  Cynthia Dwork,et al.  Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.

[6]  Xiaoqian Jiang,et al.  Differentially Private Histogram Publication for Dynamic Datasets: an Adaptive Sampling Approach , 2015, CIKM.

[7]  Junshan Zhang,et al.  Distributed Algorithms to Compute Walrasian Equilibrium in Mobile Crowdsensing , 2017, IEEE Transactions on Industrial Electronics.

[8]  Yan Zhang,et al.  RescueDP: Real-time spatio-temporal crowd-sourced data publishing with differential privacy , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[9]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[10]  Stavros Papadopoulos,et al.  Differentially Private Event Sequences over Infinite Streams , 2014, Proc. VLDB Endow..

[11]  Julia Lane,et al.  Balancing access to health data and privacy: a review of the issues and approaches for the future. , 2010, Health services research.

[12]  Cynthia Dwork,et al.  Privacy-Preserving Datamining on Vertically Partitioned Databases , 2004, CRYPTO.

[13]  Yin Yang,et al.  Differentially private histogram publication , 2012, The VLDB Journal.

[14]  Li Xiong,et al.  An Adaptive Approach to Real-Time Aggregate Monitoring With Differential Privacy , 2014, IEEE Trans. Knowl. Data Eng..

[15]  Cynthia Dwork,et al.  Differential Privacy: A Survey of Results , 2008, TAMC.