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Joshi, M., Joshi, K., Finin, T..  2018.  Attribute Based Encryption for Secure Access to Cloud Based EHR Systems. 2018 IEEE 11th International Conference on Cloud Computing (CLOUD). :932–935.
Medical organizations find it challenging to adopt cloud-based electronic medical records services, due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient centric approach for EHR management where the responsibility of authorizing data access is handled at the patients' end. This however creates a significant overhead for the patient who has to authorize every access of their health record. This is not practical given the multiple personnel involved in providing care and that at times the patient may not be in a state to provide this authorization. Hence there is a need of developing a proper authorization delegation mechanism for safe, secure and easy cloud-based EHR management. We have developed a novel, centralized, attribute based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHR's access authority to the medical providers. In this paper, we describe this novel ABE approach as well as the prototype system that we have created to illustrate it.
Ramapatruni, S., Narayanan, S. N., Mittal, S., Joshi, A., Joshi, K..  2019.  Anomaly Detection Models for Smart Home Security. 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). :19–24.
Recent years have seen significant growth in the adoption of smart homes devices. These devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized movements, and smoke sensors can detect potential fire accidents. However, many recent examples have shown that they open up a new cyber threat surface. There have been several recent examples of smart devices being hacked for privacy violations and also misused so as to perform DDoS attacks. In this paper, we explore the application of big data and machine learning to identify anomalous activities that can occur in a smart home environment. A Hidden Markov Model (HMM) is trained on network level sensor data, created from a test bed with multiple sensors and smart devices. The generated HMM model is shown to achieve an accuracy of 97% in identifying potential anomalies that indicate attacks. We present our approach to build this model and compare with other techniques available in the literature.