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Smart meters are continuously being deployed in several countries as a step in the direction of modernizing the power grid. Smart meters allow for automatic electricity consumption reporting to energy providers to facilitate billing and demand-based power generation. However, research has shown that such high resolution reporting to suppliers can potentially be used to invade consumers’ privacy; by identifying and predicting their behavior based on their consumption readings. This work presents a new protocol to preserve users’ privacy while maintaining the benefits of smart grids. The proposed method utilizes different techniques like randomization, masking, and differential privacy to build the scheme. The proposed method is shown to be more efficient compared to previous work in terms of performance and communication overhead. The implementation, simulation, and analysis are performed on datasets of real smart meters readings of households and electric vehicle chargers.

Deep brain stimulators (DBSs), a widely used and comprehensively acknowledged restorative methodology, are a type of implantable medical device which uses electrical stimulation to treat neurological disorders. These devices are widely used to treat diseases such as Parkinson, movement disorder, epilepsy, and psychiatric disorders. Security in such devices plays a vital role since it can directly affect the mental, emotional, and physical state of human bodies. In worst-case situations, it can even lead to the patient's death. An adversary in such devices, for instance, can inhibit the normal functionality of the brain by introducing fake stimulation inside the human brain. Nonetheless, the adversary can impair the motor functions, alter impulse control, induce pain, or even modify the emotional pattern of the patient by giving fake stimulations through DBSs. This paper presents a deep learning methodology to predict different attack stimulations in DBSs. The proposed work uses long short-term memory, a type of recurrent network for forecasting and predicting rest tremor velocity. (A type of characteristic observed to evaluate the intensity of the neurological diseases) The prediction helps in diagnosing fake versus genuine stimulations. The effect of deep brain stimulation was tested on Parkinson tremor patients. The proposed methodology was able to detect different types of emulated attack patterns efficiently and thereby notifying the patient about the possible attack.

Diabetic patients use therapy from the insulin pump, a type of implantable medical device, for the infusion of insulin to control blood glucose level. While these devices offer many clinical benefits, there has been a recent increase in the number of cases, wherein, the wireless communication channel of such devices has been compromised. This not only causes the device to malfunction but also potentially threatens the patient's life. In this paper, a neural networks-based multi-layer perceptron model was designed for real-time medical device security. Machine learning algorithms are among the most effective and broadly utilized systems for classification, identification, and segmentation. Although they are effective, they are both computationally and memory intensive, making them hard to be deployed on low-power embedded frameworks. In this paper, we present an on-chip neural system network for securing diabetic treatment. The model achieved 98.1% accuracy in classifying fake versus genuine glucose measurements. The proposed model was comparatively evaluated with a linear support vector machine which achieved only 90.17% accuracy with negligible precision and recall. Moreover, the proposal estimates the reliability of the framework through the use of the Bayesian network. The proposed approach enhances the reliability of the overall framework by 18% when only one device is secured, and over 90% when all devices are secured.