Biblio

With the exponential growth in the usage of electronic medical records (EMR), the amount of data generated by the healthcare industry has too increased exponentially. These large amounts of data, known as “Big Data” is mostly unstructured. Special big data analytics methods are required to process the information and retrieve information which is meaningful. As patient information in hospitals and other healthcare facilities become increasingly electronic, Big Data technologies are needed now more than ever to manage and understand this data. In addition, this information tends to be quite sensitive and needs a highly secure environment. However, current security algorithms are hard to be implemented because it would take a huge amount of time and resources. Security protocols in Big data are also not adequate in protecting sensitive information in the healthcare. As a result, the healthcare data is both heterogeneous and insecure. As a solution we propose the Secure Pattern-Based Data Sensitivity Framework (PBDSF), that uses machine learning mechanisms to identify the common set of attributes of patient data, data frequency, various patterns of codes used to identify specific conditions to secure sensitive information. The framework uses Hadoop and is built on Hadoop Distributed File System (HDFS) as a basis for our clusters of machines to process Big Data, and perform tasks such as identifying sensitive information in a huge amount of data and encrypting data that are identified to be sensitive.

Channel state information (CSI) has been recently shown to be useful in performing security attacks in public WiFi environments. By analyzing how CSI is affected by the finger motions, CSI-based attacks can effectively reconstruct text-based passwords and locking patterns. This paper presents WiGuard, a novel system to protect sensitive on-screen gestures in a public place. Our approach carefully exploits the WiFi channel interference to introduce noise into the attacker's CSI measurement to reduce the success rate of the attack. Our approach automatically detects when a CSI-based attack happens. We evaluate our approach by applying it to protect text-based passwords and pattern locks on mobile devices. Experimental results show that our approach is able to reduce the success rate of CSI attacks from 92% to 42% for text-based passwords and from 82% to 22% for pattern lock.

Cloud computing paradigm is being used because of its low up-front cost. In recent years, even mobile phone users store their data at Cloud. Customer information stored at Cloud needs to be protected against potential intruders as well as cloud service provider. There is threat to the data in transit and data at cloud due to different possible attacks. Organizations are transferring important information to the Cloud that increases concern over security of data. Cryptography is common approach to protect the sensitive information in Cloud. Cryptography involves managing encryption and decryption keys. In this paper, we compare key management methods, apply key management methods to various cloud environments and analyze symmetric key cryptography algorithms.