@conference {262, title = {Optimized and Secured Transmission and Retrieval of Vital Signs from Remote Devices}, booktitle = {2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)}, year = {2017}, month = {July}, abstract = {

Smartphones and other mobile platforms provide a low cost and easily accessible method of monitoring patient health, and aid healthcare professionals in early detection of disease. Immediate access to the gathered data is an essential factor in effective patient care. But the current processes used for patients{\textquoteright} vital data collection is slow and error prone. This undermines the advantages of remote monitoring that mobile platforms for health monitoring provide. In this paper, we propose to upload the patient health information to the Cloud. We investigate three different models to transfer data from the smartphone to the Cloud-perform all computations in the smartphone, perform all computations in the Cloud, and divide the computations between the smartphone and the Cloud. The second approach was found to be infeasible due to very high latency in data transfer with a delay of 2.84 seconds at an upload speed of 2500 KBytes per second. In order to protect the privacy of patients, it is required by law that the data gathered from remote monitoring by using mobile platforms must be kept private, and be secured before uploading to the Cloud. This paper explores the use of prominent public key encryption algorithms and their performance on a mobile device to securely transmit confidential electronic personal health information to the Cloud. We analyze performance of three common public key encryption schemes -RSA, Diffie-Hellman, and ECC. It is shown that 160 bit key size in ECC scheme provides the same level of security that a 1024 bit key size does in RSA and Diffie-Hellman. Further, the encryption and decryption time required by ECC is three times less than the other two schemes. Hence, ECC not only requires a smaller key size to provide the same level of security, but also faster encryption and decryption times as compared to the other two schemes. This makes ECC algorithms suitable to be implemented in resource constrained mobile platforms. We also compared ECC curves from three different standards - NIST, SECG, and Brainpool - to determine the optimum ECC curve, and key size to encrypt data in the mobile phone platform. It is shown that the Brainpool curve performed better than the other two standards when the key size is less than 521 bits. We also measured the latency of uploading encrypted data in a wide variety of WiFi and mobile networks.

}, keywords = {Brainpool curve, Cloud computing, confidential electronic personal health information, data transfer, decryption, Diffie-Hellman, Diseases, ECC algorithms, ECC curves, encryption, error prone, health care, healthcare professionals, Mobile communication, mobile device, mobile health, mobile networks, mobile phone platform, patient care, patient health information, patient health monitoring, patient vital data collection, Performance evaluation, public key cryptography, public key encryption algorithms, remote devices, remote monitoring, resource constrained mobile platforms, RSA, secured transmission, Security, servers, smart phones, smartphones, vital signs, WiFi}, doi = {10.1109/CHASE.2017.55}, author = {Thiyagaraja, Shanti and Ram Dantu and Shrestha, Pradhumna and Thompson, Mark and Smith, Christopher} } @conference {88, title = {A framework for secured collaboration in mHealth}, booktitle = {Collaboration Technologies and Systems (CTS), 2015 International Conference on}, year = {2015}, month = {06/2015}, abstract = {

We have designed a novel framework of services, protocols and technologies to ensure the secure collaboration in M2M networks, specifically in mobile health. The promise of mobile health to reform preventive self-care opens new doors for remote monitoring to improve health care communication. With cardiopulmonary resuscitation (CPR) as an example, we classify our M2M elements into services, roles, human-computer protocols and technologies through which we require trust, anonymity, scalability, and active detachment. We simulate a scenario in which a patient needs CPR and through the use of widely available technologies (such as a smartphone and secure web sockets) we demonstrate a technological collaboration that facilitates secure emergency mobile health services.

}, keywords = {cardiology, cardiopulmonary resuscitation, Cardiopulmonary resuscitation(CPR), Collaboration, Computers, CPR, health care, health care communication, human computer interaction, human-computer protocol, M2M network, machine to machine(M2M), Medical services, mHealth, Mobile communication, mobile computing, mobile health, Protocols, remote monitoring, Scalability, secure collaboration, secure emergency mobile health services, secured collaboration, security of data, servers, smart phones, smartphone}, doi = {10.1109/CTS.2015.7210450}, author = {Widick, Logan and Talkington, Josh and Bajwa, Garima and Ram Dantu} } @conference {233, title = {Pass-pic: A mobile user authentication}, booktitle = {Intelligence and Security Informatics (ISI), 2015 IEEE International Conference on}, year = {2015}, month = {May}, abstract = {

Conventional authentication methods utilizing alphanumeric username and passwords, PIN numbers, or any combination thereof have many weaknesses. On modern smart phones there are multiple ways users can authenticate. The traditional username password combination is used often as well as PIN passwords and pattern based passwords. The problem with these methods is that they are still vulnerable. A short password or to a much greater extent a PIN, or a pattern password can be defeated by various techniques such as smudge attacks, key loggers and so on. Our aim with Pass-Pic is to implement a picture based authentication system that is both more secure and easier for the user to both input and remember.

}, keywords = {alphanumeric username, Authentication, key loggers, Keyboards, message authentication, mobile authentication, Mobile communication, mobile user authentication, pass-pic, passwords, pattern based passwords, picture based authentication system, PIN numbers, Random access memory, Security, smart phones, smudge attacks, Vibrations}, doi = {10.1109/ISI.2015.7165977}, author = {Bajwa, Garima and Ram Dantu and Aldridge, Ryan} } @conference {232, title = {Random anonymization of mobile sensor data: Modified Android framework}, booktitle = {Intelligence and Security Informatics (ISI), 2015 IEEE International Conference on}, year = {2015}, month = {May}, abstract = {

With the increasing ability to accurately classify activities of mobile users from what was once viewed as innocuous mobile sensor data, the risk of users compromising their privacy has risen exponentially. Currently, mobile owners cannot control how various applications handle the privacy of their sensor data, or even determine if a service provider is adversarial or trustworthy. To address these privacy concerns, third party applications have been designed to allow mobile users to have control over the data that is sent to service providers. However, these applications require users to set flags and parameters that place restrictions on the anonymized or real sensor data that is sent to the requestor. Therefore, in this paper, we introduce a new framework, RANDSOM, that moves the decision-making from the application level to the operating system level.

}, keywords = {Accelerometers, Android, anonymized, application level, Data models, data privacy, Hidden Markov models, Mobile communication, mobile computing, mobile sensor data, mobile users, modified Android framework, operating system level, pervasive, Privacy, privacy concerns, provider, random anonymization, RANDSOM, RANDSOM framework, sensor data privacy, service providers, smart phone, smart phones, telecommunication security, third party applications}, doi = {10.1109/ISI.2015.7165968}, author = {Claiborne, Cynthia and Ncube, Cathy and Ram Dantu} } @conference {67, title = {Another free app: Does it have the right intentions?}, booktitle = {Privacy, Security and Trust (PST), 2014 Twelfth Annual International Conference on}, year = {2014}, month = {July}, abstract = {

Security and privacy holds a great importance in mobile devices due to the escalated use of smart phone applications (app). This has made the user even more vulnerable to malicious attacks than ever before. We aim to address this problem by proposing a novel framework to identify potential Android malware apps by extracting the intention and their permission requests. First, we constructed a dataset consisting of 1,730 benign apps along with 273 malware samples. Then, both datasets were subjected to source code extraction. From there on, we followed a two phase approach to identify potential malware samples. In phase 1, we constructed a machine learning model to group benign apps into different clusters based on their operations known as the task-intention. Once we trained the model, it was used to identify the task-intention of an Android app. Further, in this phase, we only used the benign apps to construct the task-intentions and none of the malware signatures were involved. Therefore, our approach does not use machine learning models to identify malware apps. Then, for each task-intention group, we extracted the permission-requests of the apps and constructed the probability mass functions (PMF). We named the shape of this PMF as Intention-Shape or I-Shape. In phase 2, we used the permission-requests, task-intentions and I-Shapes to identify potential malware apps. We compared the permission-requests of an unknown app with its corresponding I-Shape to identify the potential malware apps. Using this approach, we obtained an accuracy of 89\% in detecting potential malware samples. The novelty of our work is to perform potential malware identification without training any models with malware signatures, and utilization of I-Shapes to identify such potential malware samples. Our approach can be utilized to identify the safety of an app before it is installed as it performs static code analysis. Further, it can be utilized in pre-screening or multi-layer security sys- ems. It is also highly useful in screening malware apps when launching in Android markets.

}, keywords = {Android app task-intention identification, Android malware apps, Androids, benign apps, clusters, Feature extraction, Humanoid robots, I-Shape, intention-shape, invasive software, learning (artificial intelligence), machine learning model, malicious attacks, Malware, Mathematical model, mobile computing, permission-requests, PMF, probability mass functions, Shape, smart phone applications, smart phones, source code (software), source code extraction, static code analysis, Unsupervised learning}, doi = {10.1109/PST.2014.6890950}, author = {Fazeen, Mohamed and Ram Dantu} } @article {98, title = {Effective CPR Procedure With Real Time Evaluation and Feedback Using Smartphones}, journal = {IEEE Journal of Translational Engineering in Health and Medicine}, volume = {2}, year = {2014}, pages = {1-11}, abstract = {

Timely cardio pulmonary resuscitation (CPR) can mean the difference between life and death. A trained person may not be available at emergency sites to give CPR. Normally, a 9-1-1 operator gives verbal instructions over the phone to a person giving CPR. In this paper, we discuss the use of smartphones to assist in administering CPR more efficiently and accurately. The two important CPR parameters are the frequency and depth of compressions. In this paper, we used smartphones to calculate these factors and to give real-time guidance to improve CPR. In addition, we used an application to measure oxygen saturation in blood. If blood oxygen saturation falls below an acceptable threshold, the person giving CPR can be asked to do mouth-to-mouth breathing. The 9-1-1 operator receives this information real time and can further guide the person giving CPR. Our experiments show accuracy \>90\% for compression frequency, depth, and oxygen saturation.

}, keywords = {Acceleration, Accelerometers, blood, blood oxygen saturation, cardio pulmonary resuscitation, cardiology, compression depth, compression frequency, CPR, CPR parameters, CPR procedure, depth of compression, Feedback, Frequency measurement, frequency of compression, medical computing, mouth-to-mouth breathing, Oxygen, oxygen saturation, pneumodynamics, real-time evaluation, real-time systems, smart phones, smartphones, verbal instructions}, issn = {2168-2372}, doi = {10.1109/JTEHM.2014.2327612}, author = {Gupta, Neeraj and Dantu, Vishnu and Ram Dantu} } @conference {216, title = {Smart phone monitoring of second heart sound split}, booktitle = {2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2014}, month = {Aug}, abstract = {

Heart Auscultation (listening to heart sounds) is the basic element of cardiac diagnosis. The interpretation of these sounds is a difficult skill to acquire. In this work we have developed an application to detect, monitor, and analyze the split in second heart sound (S2) using a smart phone. The application records the heartbeat using a stethoscope connected to the smart phone. The audio signal is converted into the frequency domain using Fast Fourier Transform to detect the first and second heart sounds (S1 and S2). S2 is extracted and fed into the Discrete Wavelet Transform (DWT) and then to Continuous Wavelet Transform (CWT) to detect the Aortic (A2) and the Pulmonic (P2) components, which are used to calculate the split in S2. With our application, users can continuously monitor their second heart sound irrespective of ages and check for a split in their hearts with a low-cost, easily available equipment.

}, keywords = {acoustic signal processing, aortic component, biomedical equipment, Biomedical monitoring, cardiac diagnosis, cardiology, continuous wavelet transform, Continuous wavelet transforms, discrete wavelet transform, discrete wavelet transforms, fast Fourier transform, fast Fourier transforms, frequency domain, Heart, heart auscultation, heartbeat recording, medical signal processing, Monitoring, patient diagnosis, patient monitoring, pulmonic component, second heart sound split, smart phone monitoring, smart phones, stethoscope}, doi = {10.1109/EMBC.2014.6944050}, author = {Thiyagaraja, Shanti and Vempati, Jagannadh and Ram Dantu and Sarma, Tom and Dantu, Siva} } @conference {92, title = {Tire-road friction estimation utilizing smartphones}, booktitle = {Information Reuse and Integration (IRI), 2014 IEEE 15th International Conference on}, year = {2014}, month = {08/2014}, abstract = {

Tire-road friction is an important parameter for a number of different safety features present in modern-day vehicles, and the knowledge of this friction may also prove useful to the driver of a vehicle while it is in motion. In particular, this information may help inform a driver of dangerous low-traction situations that he or she may need to be aware of. Furthermore, since a growing number of drivers have access to Bluetooth-enabled smartphones, it is worth exploring how these devices may be leveraged in concert with vehicular CAN-bus networks to provide valuable safety information.

}, keywords = {Acceleration, Bluetooth, bluetooth-enabled smart phones, CAN-bus, controller area networks, dangerous low-traction situations, driver information systems, Estimation, friction, graphical user interfaces, mobile computing, road safety, road vehicles, Roads, Safety, safety information, smart phones, tire-road friction estimation, traction, tyres, vehicle safety features, Vehicles, vehicular CAN-bus networks, vehicular safety, Wheels}, doi = {10.1109/IRI.2014.7051980}, author = {Jaynes, Michael and Ram Dantu} } @conference {84, title = {Evaluation of Respiration Quality Using Smart Phone}, booktitle = {Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments}, year = {2013}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

Breathing is one of the vital signs that are considered important from medical point of view. The quality of breathing is evaluated by considering several factors. In this paper we present the results of experiments that use a smart phone to evaluate some of the factors to determine the quality of breathing. The accelerometer in the smart phone is used to measure the breathing. We measure subjects with normal breathing, slow breathing, fast breathing and irregular breathing. Our results show that we can evaluate the rate of breathing using a smart phone with an accuracy ranging from 95\% to 100\%. We can also evaluate the regularity and the effort of breathing.

}, keywords = {911 calls, respiration quality, smart phones}, isbn = {978-1-4503-1973-7}, doi = {10.1145/2504335.2504364}, url = {http://doi.acm.org/10.1145/2504335.2504364}, author = {Gupta, Neeraj and Ram Dantu} }