Manoranjan Mohanty

Secure cloud-based medical data visualization

Outsourcing the tasks of medical data visualization to cloud centers presents new security challenges. In this paper, we propose a framework for cloud-based remote medical data visualization that protects the security of data at the cloud centers. To achieve this, we integrate the cryptographic secret sharing with pre-classification volume ray-casting and propose a secure volume ray-casting pipeline that hides the color-coded information of the secret medical data during rendering at the data centers. Results and analysis show the utility of the proposed framework.

Scale me, crop me, knowme not: Supporting scaling and cropping in secret image sharing

Secret image sharing is a method for distributing a secret image amongst n data stores, each storing a shadow image of the secret, such that the original secret image can be recovered only if any k out of the n shares is available. Existing secret image sharing schemes, however, do not support scaling and cropping operations on the shadow image, which are useful for zooming on large images. In this paper, we propose an image sharing scheme that allows the user to retrieve a scaled or cropped version of the secret image by operating directly on the shadow images, therefore reducing the amount of data sent from the data stores to the user. Results and analyses show that our scheme is highly secure, requires low computational cost, and supports a large number of scale factors with arbitrary crop.

Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment

A centralized infrastructure system carries out existing data analytics and decision-making processes from our current highly virtualized platform of wireless networks and the Internet of Things (IoT) applications. There is a high possibility that these existing methods will encounter more challenges and issues in relation to network dynamics, resulting in a high overhead in the network response time, leading to latency and traffic. In order to avoid these problems in the network and achieve an optimum level of resource utilization, a new paradigm called edge computing (EC) is proposed to pave the way for the evolution of new age applications and services. With the integration of EC, the processing capabilities are pushed to the edge of network devices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation are performed which removes the necessity for a centralized system. Many IoT applications, such as smart cities, the smart grid, smart traffic lights, and smart vehicles, are rapidly upgrading their applications with EC, significantly improving response time as well as conserving network resources. Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization are still open to research studies. Hence, this paper aims to validate the efficiency and resourcefulness of EC. We extensively survey the edge systems and present a comparative study of cloud computing systems. After analyzing the different network properties in the system, the results show that EC systems perform better than cloud computing systems. Finally, the research challenges in implementing an EC system and future research directions are discussed.

Secure Cloud-Based Volume Ray-Casting

Advances in cloud computing have allowed volume rendering tasks, typically done by volume ray-casting, to be outsourced to cloud data centers. The availability of volume data and rendered images (which can contain important information such as the disease information of a patient) to a third-party cloud provider, however, presents security and privacy challenges. This paper addresses these challenges by proposing a secure cloud-based volume ray-casting framework that distributes the rendering tasks among the data centers and hides the information that is exchanged between the server and a data center, between two data centers, and between a data center and the client by using Shamirs secret sharing, such that none of the data centers has enough information to know the secret data and/or rendered image. Experiments and analyses show that our framework is highly secure and requires low computation cost.

Source camera attribution using stabilized video

Although PRNU (Photo Response Non-Uniformity)-based methods have been proposed to verify the source camera of a non-stabilized video, these methods may not be adequate for stabilized videos. The use of video stabilization has been increasing in recent years with the development of novel stabilization software and the availability of stabilization in smart-phone cameras. This paper presents a PRNU-based source camera attribution method for out-of-camera stabilized video (i.e., stabilization applied after the video is captured). The scheme can (i) automatically determine if a given video is stabilized, (ii) calculate the fingerprint from a stabilized video, and (iii) effectively correlate the fingerprint computed from a stabilized video (i.e., anonymous video) with a fingerprint computed from another stabilized or non-stabilized video (i.e., a known video). Furthermore, experimental results show that the source camera of an anonymous non-stabilized video can be verified using a fingerprint computed from a set of images.

PRNU based source attribution with a collection of seam-carved images

Photo Response Non-Uniformity (PRNU) noise based source attribution is a well known technique to verify the source camera of an anonymous image or video. Researchers have proposed various counter measures to PRNU based source camera attribution. Forced seam-carving is a recently proposed counter forensics measure that was proposed to defeat PRNU based source attribution by disturbing the alignment of PRNU noise patterns. This paper shows that given a multiple number of seam-carved images, source attribution can still be reliably made even if the size of a non-carved image block is less than the recommended size of 50×50.

Secret sharing approach for securing cloud-based pre-classification volume ray-casting

With the evolution in cloud computing, cloud-based volume rendering, which outsources data rendering tasks to cloud datacenters, is attracting interest. Although this new rendering technique has many advantages, allowing third-party access to potentially sensitive volume data raises security and privacy concerns. In this paper, we address these concerns for cloud-based pre-classification volume ray-casting by using Shamir’s (k, n) secret sharing and its variant (l, k, n) ramp secret sharing, which are homomorphic to addition and scalar multiplication operations, to hide color information of volume data/images in datacenters. To address the incompatibility issue of the modular prime operation used in secret sharing technique with the floating point operations of ray-casting, we consider excluding modular prime operation from secret sharing or converting the floating number operations of ray-casting to fixed point operations – the earlier technique degrades security and the later degrades image quality. Both these techniques, however, result in significant data overhead. To lessen the overhead at the cost of high security, we propose a modified ramp secret sharing scheme that uses the three color components in one secret sharing polynomial and replaces the shares in floating point with smaller integers.

Scaling and Cropping of Wavelet-Based Compressed Images in Hidden Domain

With the rapid advancement of cloud computing, the use of third-party cloud datacenters for storing and processing (e.g, scaling and cropping) personal and critical images is becoming more common. For storage and bandwidth efficiency, the images are almost always compressed. Although cloud-based imaging has many advantages, security and privacy remain major issues. One way to address these two issues is to use Shamir’s (k, n) secret sharing-based secret image sharing schemes, which can distribute the secret image among n number of participants in such a way that no less than k (where k ≤ n) participants can know the image content. Existing secret image sharing schemes do not allow processing of a compressed image in the hidden domain. In this paper, we propose a scheme that can scale and crop a CDF (Cohen Daubechies Feauveau) wavelet-based compressed image (such as JPEG2000) in the encrypted domain by smartly applying secret sharing on the wavelet coefficients. Results and analyses show that our scheme is highly secure and has acceptable computational and data overheads.

Avoiding weak parameters in secret image sharing

Secret image sharing is a popular image hiding scheme that typically uses (3, 3, n) multi-secret sharing to hide the colors of a secret image. The use of (3, 3, n) multi-secret sharing, however, can lead to information loss. In this paper, we study this loss of information from an image perspective, and show that one-third of the color values of the secret image can be leaked when the sum of any two selected share numbers is equal to the considered prime number in the secret sharing. Furthermore, we show that if the selected share numbers do not satisfy this condition (for example, when the value of each of the selected share number is less than the half of the value of the prime number), then the colors of the secret image are not leaked. In this case, a noise-like image is reconstructed from the knowledge of less than three shares.

Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

Heterogeneous vehicular networks (HETVNETs) evolve from vehicular ad hoc networks (VANETs), which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs). The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS) improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM) kernels with a radial basis function (RBF). The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.