Analyzing privacy breaches in Internet-based messaging applications is challenging due to overlapping and sometimes conflicting requirements such as confidentiality, anonymity, unlinkability, and user consent. Existing static analysis techniques typically target isolated aspects of privacy, limiting their scope. In this work, we introduce a static analysis framework based on a composite privacy model that captures the interdependencies among these requirements. This unified model enables the systematic identification of technical privacy violations and their associated legal implications, such as infringements of data protection laws and digital rights. We apply our framework to Ejabberd, a real-time communication server used in messaging platforms like WhatsApp. Our analysis focuses on confidentiality and consent-driven privacy concerns, including the right to be informed and the right to erasure. The results highlight the effectiveness of our approach in bridging technical analysis with legal accountability.

Privacy of Internet-based messaging applications involves establishing multiple requirements such as confidentiality, anonymity, pseudonymity, and consent, each typically addressed in isolation using different techniques. To the best of our knowledge, no unified framework supports the privacy analysis of these aspects together. Therefore, the main goal of our work is to detect privacy breaches based on a unified modelling of the relationship among the privacy requirements by static program analysis. In this paper, we present initial results on the privacy (i.e., confidentiality) analysis of Ejabberd server – an open-source XMPP backend server used by popular messaging applications like WhatsApp. Based on the confidentiality modelling, we perform a privacy analysis of key Ejabberd modules that are responsible for user authentication. The findings highlight the module implementations that involve potential privacy breaches and serve as a basis for our future work towards developing a unified privacy evaluation framework.

Home energy management systems are widely used to cope up with the increasing demand for energy. They help to reduce carbon pollutants generated by excessive burning of fuel and natural resources required for energy generation. They also save the budget needed for installing new power plants. Price based automatic demand response (DR) techniques incorporated in these systems shift appliances from high price hours to low price hours to reduce electricity bills and peak to average ratio (PAR). In this paper, electricity load of home is categorized into three types: base load, shift-able interruptible load and shiftable non-interruptible load. In literature many metaheuristic optimization techniques have been implemented for scheduling of appliances. In this work for the optimization of energy usage genetic algorithm (GA) and bat algorithm (BA) are implemented with time of use (TOU) pricing scheme to schedule appliances to reduce electricity bills, the peak to average ratio and appliance delay time. A new technique bat genetic algorithm (BGA) has been proposed. It is hybrid of GA and BA. It outperforms GA and BA in terms of cost reduction and peak to average ratio for single home scenario as well as multiple home scenario. Operation time internals (OTIs) 15 minutes, 30 minutes and 1 hour have been considered to check their effect on cost reduction, PAR and user comfort (UC).

With the emergence of automated environment, energy demand by consumer is increasing day by day. More than 80% of total electricity is being consumed in residential sector. In this paper, a heuristic optimization technique is proposed for the efficient utilization of energy sources to balance load between demand and supply sides. An optimization technique is proposed which is a hybrid of Enhanced differential evolution (EDE) algorithm and Gray wolf optimization (GWO). The proposed scheme is named as hybrid gray wolf differential evolution (HGWDE). It is applied for home energy management (HEM) with the objective function of cost minimization and reducing peak to average ratio (PAR). Load shifting is performed from on peak hours to off peak hours on basis of user preference and real time pricing (RTP) tariff defined by utility. However, there is a trade off between user comfort and above mentioned parameters. To validate the performance of proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that PAR and electricity bill have been reduced to 53.02%, and 12.81% respectively.