A privacy preserve big data analysis system for wearable wireless sensor network

Abstract Big data and artificial intelligence develop rapidly. Big data analysis has been applied in many fields of smart healthcare. Once these data are leaked or modified during transmission, it will not only invade the privacy of patients, but also endanger their lives. Many researchers worked on encrypted personal health records (PHR). However, there are still some challenges, such as data leakage during deep learning and leakage of training models, and some users do not want their data to be leaked to the analysis organization. How to protect privacy while leveraging deep learning is a pressing issue. In this paper, we present a system for predicting disease and timely alarms by collecting data from sensors and using deep learning to analyze and monitor patient health data. In order to protect the privacy of health data, we adopt an assured data deletion approach which the data owner can choose to revoke some users’ access to their health data. Extensive analysis and experimental results are presented that demonstrate the security, and efficiency of our proposed approach.

[1]  Brent Waters,et al.  Ciphertext-Policy Attribute-Based Encryption , 2007, 2007 IEEE Symposium on Security and Privacy (SP '07).

[2]  Marco Tagliasacchi,et al.  An integrated system based on wireless sensor networks for patient monitoring, localization and tracking , 2013, Ad Hoc Networks.

[3]  Ting Peng,et al.  Directly Revocable and Verifiable Key-Policy Attribute-based Encryption for Large Universe , 2017, Int. J. Netw. Secur..

[4]  Srdjan Capkun,et al.  SoK: Secure Data Deletion , 2013, 2013 IEEE Symposium on Security and Privacy.

[5]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[6]  Amit A. Levy,et al.  Vanish: Increasing Data Privacy with Self-Destructing Data , 2009, USENIX Security Symposium.

[7]  Viju Raghupathi,et al.  Big data analytics in healthcare: promise and potential , 2014, Health Information Science and Systems.

[8]  Laurence T. Yang,et al.  Secure weighted possibilistic c-means algorithm on cloud for clustering big data , 2018, Inf. Sci..

[9]  Brian Litt,et al.  Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[10]  Jianfeng Ma,et al.  A full lifecycle privacy protection scheme for sensitive data in cloud computing , 2014, Peer-to-Peer Networking and Applications.

[11]  Francesco Palmieri,et al.  On Secure Data Management in Health-Care Environment , 2013, 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing.

[12]  Jianfeng Ma,et al.  A Secure Document Self-Destruction Scheme: An ABE Approach , 2013, 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing.

[13]  Xiong Jin.  A Secure Self-Destruction Scheme with IBE for the Internet Content Privacy , 2014 .

[14]  Willy Susilo,et al.  Identity-based conditional proxy re-encryption with fine grain policy , 2017, Comput. Stand. Interfaces.

[15]  Stephen Lin,et al.  Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning , 2014, ACCV.

[16]  Aaron Carass,et al.  Deep Learning for Cerebellar Ataxia Classification and Functional Score Regression , 2014, MLMI.

[17]  Brent Waters,et al.  Defeating Vanish with Low-Cost Sybil Attacks Against Large DHTs , 2010, NDSS.

[18]  Jinyue Xia,et al.  Revocable Identity-Based Broadcast Proxy Re-Encryption for Data Sharing in Clouds , 2019, IEEE Transactions on Dependable and Secure Computing.

[19]  Yi Mu,et al.  Public Integrity Auditing for Dynamic Data Sharing With Multiuser Modification , 2015, IEEE Transactions on Information Forensics and Security.

[20]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[21]  Brent Waters,et al.  Attribute-based encryption for fine-grained access control of encrypted data , 2006, CCS '06.

[22]  Brent Waters,et al.  Fuzzy Identity-Based Encryption , 2005, EUROCRYPT.

[23]  Willy Susilo,et al.  A Ciphertext-Policy Attribute-Based Proxy Re-encryption with Chosen-Ciphertext Security , 2013, 2013 5th International Conference on Intelligent Networking and Collaborative Systems.

[24]  Alfredo De Santis,et al.  Secure group communication schemes for dynamic heterogeneous distributed computing , 2017, Future Gener. Comput. Syst..

[25]  Yacine Challal,et al.  C-CP-ABE: Cooperative Ciphertext Policy Attribute-Based Encryption for the Internet of Things , 2014, 2014 International Conference on Advanced Networking Distributed Systems and Applications.

[26]  Maruf Pasha,et al.  Survey of Machine Learning Algorithms for Disease Diagnostic , 2017 .

[27]  Alexander Schliep,et al.  Automatic Blood Glucose Prediction with Confidence Using Recurrent Neural Networks , 2018, KDH@IJCAI.

[28]  Qin Liu,et al.  A Secure Self-Destructing Scheme for Electronic Data , 2010, 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing.

[29]  Xiaojiang Du,et al.  Efficient attribute-based encryption with attribute revocation for assured data deletion , 2018, Inf. Sci..

[30]  Dan Feng,et al.  SeDas: A Self-Destructing Data System Based on Active Storage Framework , 2013, IEEE Transactions on Magnetics.

[31]  Dan Boneh,et al.  Attribute-Based Encryption for Arithmetic Circuits , 2013, IACR Cryptol. ePrint Arch..

[32]  Willy Susilo,et al.  A CCA-secure key-policy attribute-based proxy re-encryption in the adaptive corruption model for dropbox data sharing system , 2018, Designs, Codes and Cryptography.

[33]  Yong Yu,et al.  Identity-Based Remote Data Integrity Checking With Perfect Data Privacy Preserving for Cloud Storage , 2017, IEEE Transactions on Information Forensics and Security.

[34]  Hubert Ritzdorf,et al.  Secure data deletion from persistent media , 2013, CCS.

[35]  Bu-Sung Lee,et al.  How to Track Your Data: Rule-Based Data Provenance Tracing Algorithms , 2012, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications.

[36]  Jenna Wiens,et al.  Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories , 2018, KDD.

[37]  Yacine Challal,et al.  Collaborative KP-ABE for cloud-based Internet of Things applications , 2016, 2016 IEEE International Conference on Communications (ICC).

[38]  Jiqiang Liu,et al.  Directly revocable key-policy attribute-based encryption with verifiable ciphertext delegation , 2015, Inf. Sci..

[39]  Pedro Castillejo,et al.  Integration of wearable devices in a wireless sensor network for an E-health application , 2013, IEEE Wireless Communications.

[40]  Xiaohui Liang,et al.  Attribute based proxy re-encryption with delegating capabilities , 2009, ASIACCS '09.