Displaying all 3 publications

Abstract:
Sort:
  1. Fulltext SenseCrypt: A Security Framework for Mobile Crowd Sensing Applications
    Pius Owoh N, Mahinderjit Singh M
    Sensors (Basel), 2020 Jun 09;20(11).
    PMID: 32526843 DOI: 10.3390/s20113280
    The proliferation of mobile devices such as smartphones and tablets with embedded sensors and communication features has led to the introduction of a novel sensing paradigm called mobile crowd sensing. Despite its opportunities and advantages over traditional wireless sensor networks, mobile crowd sensing still faces security and privacy issues, among other challenges. Specifically, the security and privacy of sensitive location information of users remain lingering issues, considering the "on" and "off" state of global positioning system sensor in smartphones. To address this problem, this paper proposes "SenseCrypt", a framework that automatically annotates and signcrypts sensitive location information of mobile crowd sensing users. The framework relies on K-means algorithm and a certificateless aggregate signcryption scheme (CLASC). It incorporates spatial coding as the data compression technique and message query telemetry transport as the messaging protocol. Results presented in this paper show that the proposed framework incurs low computational cost and communication overhead. Also, the framework is robust against privileged insider attack, replay and forgery attacks. Confidentiality, integrity and non-repudiation are security services offered by the proposed framework.
  2. Fulltext Breast Self-Examination System Using Multifaceted Trustworthiness: Observational Study
    Khana R, Mahinderjit Singh M, Damanhoori F, Mustaffa N
    JMIR Med Inform, 2020 Sep 23;8(9):e21584.
    PMID: 32965225 DOI: 10.2196/21584
    BACKGROUND: Breast cancer is the leading cause of mortality among women worldwide. However, female patients often feel reluctant and embarrassed about meeting physicians in person to discuss their intimate body parts, and prefer to use social media for such interactions. Indeed, the number of patients and physicians interacting and seeking information related to breast cancer on social media has been growing. However, a physician may behave inappropriately on social media by sharing a patient's personal medical data excessively with colleagues or the public. Such an act would reduce the physician's trustworthiness from the patient's perspective. The multifaceted trust model is currently most commonly used for investigating social media interactions, which facilitates its enhanced adoption in the context of breast self-examination. The characteristics of the multifaceted trust model go beyond being personalized, context-dependent, and transitive. This model is more user-centric, which allows any user to evaluate the interaction process. Thus, in this study, we explored and evaluated use of the multifaceted trust model for breast self-examination as a more suitable trust model for patient-physician social media interactions in breast cancer screening.

    OBJECTIVE: The objectives of this study were: (1) to identify the trustworthiness indicators that are suitable for a breast self-examination system, (2) design and propose a breast self-examination system, and (3) evaluate the multifaceted trustworthiness interaction between patients and physicians.

    METHODS: We used a qualitative study design based on open-ended interviews with 32 participants (16 outpatients and 16 physicians). The interview started with an introduction to the research objective and an explanation of the steps on how to use the proposed breast self-examination system. The breast self-examination system was then evaluated by asking the patient to rate their trustworthiness with the physician after the consultation. The evaluation was also based on monitoring the activity in the chat room (interactions between physicians and patients) during daily meetings, weekly meetings, and the articles posted by the physician in the forum.

    RESULTS: Based on the interview sessions with 16 physicians and 16 patients on using the breast self-examination system, honesty had a strong positive correlation (r=0.91) with trustworthiness, followed by credibility (r=0.85), confidence (r=0.79), and faith (r=0.79). In addition, belief (r=0.75), competency (r=0.73), and reliability (r=0.73) were strongly correlated with trustworthiness, with the lowest correlation found for reputation (r=0.72). The correlation among trustworthiness indicators was significant (P

  3. Fulltext Automatic Annotation of Unlabeled Data from Smartphone-Based Motion and Location Sensors
    Pius Owoh N, Mahinderjit Singh M, Zaaba ZF
    Sensors (Basel), 2018 Jul 03;18(7).
    PMID: 29970823 DOI: 10.3390/s18072134
    Automatic data annotation eliminates most of the challenges we faced due to the manual methods of annotating sensor data. It significantly improves users’ experience during sensing activities since their active involvement in the labeling process is reduced. An unsupervised learning technique such as clustering can be used to automatically annotate sensor data. However, the lingering issue with clustering is the validation of generated clusters. In this paper, we adopted the k-means clustering algorithm for annotating unlabeled sensor data for the purpose of detecting sensitive location information of mobile crowd sensing users. Furthermore, we proposed a cluster validation index for the k-means algorithm, which is based on Multiple Pair-Frequency. Thereafter, we trained three classifiers (Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes) using cluster labels generated from the k-means clustering algorithm. The accuracy, precision, and recall of these classifiers were evaluated during the classification of “non-sensitive” and “sensitive” data from motion and location sensors. Very high accuracy scores were recorded from Support Vector Machine and K-Nearest Neighbor classifiers while a fairly high accuracy score was recorded from the Naïve Bayes classifier. With the hybridized machine learning (unsupervised and supervised) technique presented in this paper, unlabeled sensor data was automatically annotated and then classified.
Related Terms
    Try searching for something
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links