Confidentiality Of Private Data Research Articles

Synchronization of Chaotic Systems and Its Application in Security Terminal Sensing Node of Internet of Things.

Recently, with the rapid development of data and information, it has become necessary to establish secure communications and appropriate security services to ensure a secure information exchange process. Therefore, to protect the privacy and confidentiality of private data, in this research, we use the Lorenz chaotic system to generate chaotic signals and apply them to the encryption of the communication of the Internet of Things (IoT) terminal sensor nodes. In addition, we design a simple proportional-integral-derivative (PID) controller and a quasi-sliding mode controller (QSMC) to synchronize the master-slave chaotic systems for decrypting the signals. Then, we encrypt the environmental signals measured from the IoT node at the transmitting side (master) and send them to the receiving side (slave). After the receiving side receives the encrypted signals, it decrypts them with the PID controller. Thus, the security of IoT information can be assured and realized.

A novel cloud-edge collaboration based short-term load forecasting method for smart grid

With the increasing development of smart grid technology, short-term load forecasting becomes particularly important in power system operation. However, the design of accurate and reliable short-term load forecasting methods and models is challenging due to the volatility and intermittency of renewable energy sources, as well as the privacy and individual characteristics of electricity consumption data from user data. To overcome this issue, in this paper, a novel cloud-edge collaboration short-term load forecasting method is proposed for smart grid. In order to reduce the computational load of edge nodes and improve the accuracy of node prediction, we use the method of building a model pre-training pool to train multiple pre-training models in the cloud layer at the same time. Then we use edge nodes to retrain the pre-trained model, select the optimal model and update the model parameters to achieve short-term load forecasting. To assure the validity of the model and the confidentiality of private data, we utilize the model pre-training pool to minimize edge node training difficulty and employ the approach of secondary edge node training. Finally, extensive experiments confirm the efficacy of our proposed method.

ConvNet for Finger Vein based Personal Authentication

The human brain, can easily perceive and differentiate the objects in an image. Subsequently the field of computer vision intent to mimic / simulate the human vision system. Finger vein-based user authentication has been used to control access and maintaining privacy of confidential data. The main challenges in the finger vein verification are the quality of an acquired images due to uneven illumination of light, quality of sensor, positional variation and environmental condition. In this article, we used Wiener filter, to improve the quality of finger vein images. These noise free images are provided for training to popular pretrained ConvNet architecture for user verification using finger vein biometric. Then we analysed the performance of ConvNet (convolutional neural networks) such as Alex Net, Squeeze Net, Google Net, Shuffle Net, Efficient Net, Mobile Net, Res Net, Dense Net and NASNet for the finger vein based personal authentication to secure confidential data and maintain privacy. The finger vein images from Kaggle database is used for this research work. The experiment exhibits the outstanding performance of resnet101 with the 97.64% accuracy over its peer networks.

A Secure Two Factor Authentication Protocol for Cloud-Assisted Wireless Body Area Network Using Blockchain

The recent advancements in technologies have allowed us to come so far and resulted in many breakthroughs. One of the various examples is internet of things, wireless communication, and cloud computing which can be useful if utilize in many fields. In the field of medical, these advancements allowed any medical centres to improve patient’s health remotely simply by using wearable devices on patients that then will amalgamate with the wireless body area network (WBAN). However, WBAN has limited resources which limits its services. To solve this problem, cloud computing is used to provide storage and computation. Unfortunately, these methods allow the system to be vulnerable to various malicious attacks. Attackers can easily gain access to the medical records of patients hence the integrity of security and privacy of confidential data have been compromised. In this paper, we presented a secure protocol for cloud-assisted database using multi-factor authentication and blockchain as an added measure to ensure security. Accordingly, we prove that the presented scheme offers more security and privacy. Therefore, it is the most practical method to be applied in the medical field.

HHDSSC: harnessing healthcare data security in cloud using ciphertext policy attribute-based encryption

The advancement of cloud computing has great impact on the medical sector. Due to its storage facility, e-healthcare has emerged as a promising healthcare solution for providing fast and immediate treatment to patients. The PHRs collected and outsourced in the cloud leads to security concern. The data outsourced in the cloud is no more under the direct control of the patient, hence data should be encrypted prior its storage. Existing works based on group signature require high amount of computation. Other issues like confidentiality of private data, efficient key distribution, scalable and flexible fine-grained data access, revocation and tracing the malicious user is yet to be addressed to maintain the integrity of the patients. In this manuscript, we propose EPOC-1-based multi authority CP-ABE which can trace and revoke the malicious user who leaks the real identity and confidential data of the patient without any storage overhead. This methodology of white-box traceability presented in this manuscript, traces the malicious user efficiently. The proposed scheme is validated with some existing policies and makes the healthcare domain more securable under the cloud setup.

Steganography: Step Towards Security and Privacy of Confidential Data in Insecure Medium by Using LSB and Cover Media

Today, in the digital world, all the things are available on internet. The main concern is about privacy and security due to which steganography has experienced a great concentration by the researcher, as it has been an effective method to provide data security or secure communication. There are many modern and successful methods to secure digital data in a real-world application. Cost effective secrecy can be accomplished by doing the steganography methods. This paper presents the Least Significant Bit Steganography method of embedding text data in an audio file. The text will be taken as a secret message. In second case, I have taken text as a cover media and secret message itself hidden in cover media. The stegno function is based on index location. The key is used to encrypt the stegno function and send it to the receiver end. The stegno function is encoded in such a way that it can be send to the destination safely and to give a better, extremely organized method for maintaining security.

HHDSSC: harnessing healthcare data security in cloud using ciphertext policy attribute-based encryption

The advancement of cloud computing has great impact on the medical sector. Due to its storage facility, e-healthcare has emerged as a promising healthcare solution for providing fast and immediate treatment to patients. The PHRs collected and outsourced in the cloud leads to security concern. The data outsourced in the cloud is no more under the direct control of the patient, hence data should be encrypted prior its storage. Existing works based on group signature require high amount of computation. Other issues like confidentiality of private data, efficient key distribution, scalable and flexible fine-grained data access, revocation and tracing the malicious user is yet to be addressed to maintain the integrity of the patients. In this manuscript, we propose EPOC-1-based multi authority CP-ABE which can trace and revoke the malicious user who leaks the real identity and confidential data of the patient without any storage overhead. This methodology of white-box traceability presented in this manuscript, traces the malicious user efficiently. The proposed scheme is validated with some existing policies and makes the healthcare domain more securable under the cloud setup.

Utilising fog computing for developing a person-centric heart monitoring system

Heart disease and stroke are becoming the leading causes of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that help physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECGs have limited accuracy and rely on external resources to analyse the signals and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyse the signal and identify abnormal behaviour. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud, (c) the overall power consumption and (d) the confidentiality of private data. Based on this concept, the HEART system presented in this work, combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate, and instant heart monitoring. The wearable device is remotely trained by a physician to learn to accurately identify critical events related to each particular patient. Following this training session, the wearable device becomes capable of interpreting a large number of heart abnormalities without relying on cloud services and edge resources, when the medical doctor is not present. The Fog computing approach extends the cloud computing paradigm by migrating data-processing closer to production site, thus accelerating the system's responsiveness to events. The HEART system's performance concerning the accuracy of detecting abnormal events and the power consumption of the wearable device is evaluated. Results indicate that a very high success rate can be achieved in terms of event detection ratio and the battery is able to sustain operation up to a full week without the need for recharge.

A new efficient block matching data hiding method based on scanning order selection in medical images

Digital technology and the widespread use of the Internet has increased the speeds at which digital data can be obtained and shared in daily life. In parallel to this, there are important concerns regarding the confidentiality of private data during data transmissions and the possibility that data might fall into the hands of third parties. Issues relating to data safety can also affect patients' medical images and other information relating to these images. In this study, we propose a new method based on block matching that can be used to hide the patient information in medical images. In this method, 8 scanning orders (6 of which are newly designed) are developed to provide high image quality. By diversifying the number of scanning orders, we aim to achieve the lowest number of bit changes. The performance of the developed method is measured using the number of bits subject to change, the peak signal-to-noise ratio and the mean structural similarity index measure image quality assessment metrics, and steganalysis attacks. The method we developed was found to be more effective in hiding data compared to the classical least significant bit method.