No abstract

Privacy is one of the most popular technologies in the IT industry. A lot of data is collected from users from various sources. The same IoT (Internet of Things) application raised concerns about privacy in IoT systems. Sensor data collects information about users' daily activities and makes life easier, but compromises privacy and security. Implementing privacy and security into applications is a huge challenge. This article presents common methods for maintaining privacy and protecting statistics in IoT applications. To this end, many encryption methods are analysed, and finally, a comparative analysis of personal information protection methods and their applications is presented.

Cloud computing is one of the key computing platform and technology for sharing resources that may include infrastructure, software, applications, and business processes. Cloud computing incorporate within it data loss prevention, encryption, and authentication, as technologies aimed to support cloud environment. The main intention behind cloud computing is the work done on the client side that can be moved to some unseen cluster of resources over the internet. Context awareness is the process in which the system or system components gather information from its surroundings accordingly. It is responsible for collecting the data automatically and responds to the situation arising dynamically. The focus of this paper is on developing a Context Sensitive Privacy Provision Algorithm such that the encryption and decryption of the data can be done only at the user end but not at the server end so as to preserve context privacy of an individual.

Purpose There are various system techniques or models which are used for access control by performing cryptographic operations and characterizing to provide an efficient cloud and in Internet of Things (IoT) access control. Particularly in cloud computing environment, there is a large-scale distribution of these traditional symmetric cryptographic techniques. These symmetric cryptographic techniques use the same key for encryption and decryption processes. However, during the execution of these phases, they are under the problems of key distribution and management. The purpose of this study is to provide efficient key management and key distribution in cloud computing environment. Design/methodology/approach This paper uses the Cipher text-Policy Attribute-Based Encryption (CP-ABE) technique with proper access control policy which is used to provide the data owner’s control and share the data through encryption process in Cloud and IoT environment. The data are shared with the the help of cloud storage, even in presence of authorized users. The main method used in this research is Enhanced CP-ABE Serialization (E-CP-ABES) approach. Findings The results are measured by means of encryption, completion and decryption time that showed better results when compared with the existing CP-ABE technique. The comparative analysis has showed that the proposed E-CP-ABES has obtained better results of 2373 ms for completion time for 256 key lengths, whereas the existing CP-ABE has obtained 3129 ms of completion time. In addition to this, the existing Advanced Encryption Standard (AES) scheme showed 3449 ms of completion time. Originality/value The proposed research work uses an E-CP-ABES access control technique that verifies the hidden attributes having a very sensitive dataset constraint and provides solution to the key management problem and access control mechanism existing in IOT and cloud computing environment. The novelty of the research is that the proposed E-CP-ABES incorporates extensible, partially hidden constraint policy by using a process known as serialization procedure and it serializes to a byte stream. Redundant residue number system is considered to remove errors that occur during the processing of bits or data obtained from the serialization. The data stream is recovered using the Deserialization process.

With the widespread use of mobile phones and smartphone applications, protecting one's privacy has become a major concern. Because active defensive strategies and temporal connections between situations relevant to users are not taken into account, present privacy preservation systems for cell phones are often ineffective. This work defines secrecy maintenance issues similar to optimizing tasks, thereby verifying their accuracy and optimization capabilities through a hypothetical study. Many optimal issues arise while preserving one's privacy and these optimal issues are to be addressed as linear programming issues. By addressing linear programming issues, an effective context-aware privacy-preserving algorithm (CAPP) was created that uses an active defence strategy to determine how to release a user's current context to enhance the quality of service (QoS) regarding context-aware applications while maintaining secrecy. CAPP outperforms other standard methodologies in lengthy simulations of actual data. Additionally, the minimax learning algorithm (MLA) optimizes the policy users and improves the satisfaction threshold of the contextaware applications. Moreover, a cloud-based approach is introduced in the work to protect the user's privacy from third parties. The obtained performance measures are compared with existing approaches in terms of privacy policy breaches, context sensitivity, satisfaction threshold, adversary power, and convergence speed for online and offline attacks.