Robust data protection and high efficiency for IoTs streams in the cloud

Remotely generated streaming of the Internet of Things (IoTs) data has become a vital category upon which many applications rely. Smart meters collect readings for household activities such as power and gas consumption every second - the readings are transmitted wirelessly through various channels and public hops to the operation centres. Due to the unusually large streams sizes, the operation centres are using cloud servers where various entities process the data on a real-time basis for billing and power management. It is possible that smart pipe projects (where oil pipes are continuously monitored using sensors) and collected streams are sent to the public cloud for real-time flawed detection. There are many other similar applications that can render the world a convenient place which result in climate change mitigation and transportation improvement to name a few. Despite the obvious advantages of these applications, some unique challenges arise posing some questions regarding a suitable balance between guaranteeing the streams security, such as privacy, authenticity and integrity, while not hindering the direct operations on those streams, while also handling data management issues, such as the volume of protected streams during transmission and storage. These challenges become more complicated when the streams reside on third-party cloud servers. In this thesis, a few novel techniques are introduced to address these problems. We begin by protecting the privacy and authenticity of transmitted readings without disrupting the direct operations. We propose two steganography techniques that rely on different mathematical security models. The results look promising - security: only the approved party who has the required security tokens can retrieve the hidden secret, and distortion effect with the difference between the original and protected readings that are almost at zero. This means the streams can be used in their protected form at intermediate hops or third party servers. We then improved the integrity of the transmitted protected streams which are prone to intentional or unintentional noise - we proposed a secure error detection and correction based stenographic technique. This allows legitimate recipients to (1) detect and recover any noise loss from the hidden sensitive information without privacy disclosure, and (2) remedy the received protected readings by using the corrected version of the secret hidden data. It is evident from the experiments that our technique has robust recovery capabilities (i.e. Root Mean Square (RMS) <0.01%, Bit Error Rate (BER) = 0 and PRD < 1%). To solve the issue of huge transmitted protected streams, two compression algorithms for lossless IoTs readings are introduced to ensure the volume of protected readings at intermediate hops is reduced without revealing the hidden secrets. The first uses Gaussian approximation function to represent IoTs streams in a few parameters regardless of the roughness in the signal. The second reduces the randomness of the IoTs streams into a smaller finite field by splitting to enhance repetition and avoiding the floating operations round errors issues. Under the same conditions, our both techniques were superior to existing models mathematically (i.e. the entropy was halved) and empirically (i.e. achieved ratio was 3.8:1 to 4.5:1). We were driven by the question ‘Can the size of multi-incoming compressed protected streams be re-reduced on the cloud without decompression?’ to overcome the issue of vast quantities of compressed and protected IoTs streams on the cloud. A novel lossless size reduction algorithm was introduced to prove the possibility of reducing the size of already compressed IoTs protected readings. This is successfully achieved by employing similarity measurements to classify the compressed streams into subsets in order to reduce the effect of uncorrelated compressed streams. The values of every subset was treated independently for further reduction. Both mathematical and empirical experiments proved the possibility of enhancing the entropy (i.e. almost reduced by 50%) and the resultant size reduction (i.e. up to 2:1)

Wavelet based steganographic technique to protect household confidential information and seal the transmitted smart grid readings

Smart grids have recently drawn attention because of high efficiency, reliability and sustainability. They transmit (1) periodically collected readings (e.g. watts) and (2) highly sensitive data (e.g. geometric location). However, transmission and storage of smart grid data have many security issues. This paper proposes a novel steganographic technique that guarantees (1) strong end-to-end confidentiality of the sensitive information by hiding them randomly inside the normal readings using a generated key, and (2) robust authenticity for the transmitted readings. To facilitate hiding, Discrete Wavelet Transform is used to decompose normal readings into a set of sub-band coefficients. To achieve minimum distortion, only the least featured sub-band coefficients are used. To achieve high security, a key is used to generate a random hiding order in the form of 2D matrix which allows the system to specify exact locations in the wavelet generated 2D coefficients' matrix to hide sensitive data. To accurately measure the distortion after hiding and retrieving the sensitive data, PRD has been used. It is clear from experiments that our technique has little effect on the original readings (<1%). Also, our security evaluation proves that unauthorised retrieval of the confidential information is highly improbable within a reasonable time

Gaussian approximation based lossless compression of smart meter readings

Automation metering services, load forecasting and energy feedback are among the great benefits of smart meters. These meters are usually connected using Narrowband power Line Communication (PLC) to transmit the collected waveform readings. The huge volume of these streams, the limited-bandwidth, energy and required storage space pose a unique management challenge. Compression of these streams has a significant opportunity to solve these issues. Therefore, this paper proposes a new lossless smart meter readings compression algorithm. The uniqueness is in representing smart meter streams using few parameters. This is effectively achieved using Gaussian approximation based on dynamic-nonlinear learning technique. The margin space between the approximated and the actual readings is measured. The significance is that the compression will be only for margin space limited points rather than the entire stream of readings. The margin space values are then encoded using Burrow-Wheeler Transform followed by Move-To-Front and Run-Length to eliminate the redundancy. Entropy encoding is finally applied. Both mathematical and empirical experiments have been thoroughly conducted to prove the significant enhancement of the entropy (i.e. almost reduced by half) and the resultant compression ratio (i.e. 3.8:1) which is higher than any known lossless algorithm in this domain

Resilient to shared spectrum noise scheme for protecting cognitive radio smart grid readings - BCH based steganographic approach

Cognitive Radio smart grids have recently attracted attention because of high efficiency and throughput performance. They transmit (1) periodically collected readings (e.g. monitoring) and (2) highly sensitive data (e.g. geometric location). However, robustness, efficiency and security of the transmitted data compose an unaddressed unique challenge due to CR shared spectrum possible noise. This paper proposes the first novel hybrid model that combines advanced steganographic algorithms with error detection and correction techniques (BCH syndrome codes) in the CR smart meter context. This will allow us to (a) detect and recover any loss from the hidden confidential information without privacy disclosure, and (b) remedy the received normal readings by using the corrected version of the secret hidden data. To randomize hiding and minimize the distortion, 3D wavelet is used to decompose normal readings into a set of coefficients. To strengthen the security, a key is utilized to generate a 3D randomly selected order used in the hiding process. To accurately measure the detection and recovery capabilities, random noise levels are applied to the transmitted readings. The recovered sensitive information and stego readings are extensively measured using BER, PRD and RMS. It is obvious from the experiments that our technique has robust recovery capabilities (i.e. BER = 0, PRD < 1% and RMS < 0.01%)