Research of Query Verification Algorithm on Body Sensing Data in Cloud Computing Environment

Conventional cloud computing iot encrypted data comparative analysis method to simple encryption scheme comparison, the accuracy of the scheme comparison is not high, for more complex data encryption scheme comparison, comparative stability lower deficiencies, therefore put forward under the cloud computing environment, the Internet of things encrypted data comparison analysis research. Introducing the sliding window technology, build the Internet of things to encrypt data security evaluation mechanism, determine the iot encrypted data comparison analysis algorithm, constructing iot encrypted data comparison analysis model is presented. Run the analysis model, analyze the iot encrypted data comparison scheme, and implement the iot encrypted data comparison scheme analysis. The experimental data show that the proposed scheme is more routine than the conventional scheme analysis, and the stability of the method is maintained at 70%–90%, which is suitable for the comparison scheme analysis of the network encryption data with different difficulty coefficients. The proposed method for comparative analysis of IoT encrypted data is highly effective.

Managing Big Data in Cloud Computing Environments

[This retracts the article DOI: 10.1155/2022/9709101.].

In recent years, the growth of data is generated at a very high speed by number of consumers. Cloud Computing comes up with a solution of storing that data without expanding so much cost. Deduplication is finding the duplicate data when compared with one or more data base or data sets. This paper explained and categorizes data deduplication techniques applied on stored data in distributed computing condition to provide security when database size expands. In this study the authors elaborated the coordinating procedures of scrambled information and open difficulties for information deduplication.

Technological evolvement of Wireless Sensor Networks (WSNs) gave birth to an attractive research area for health monitoring called Body Sensor Network (BSN). In BSN tiny sensor nodes sense physiological data of patients under medical health care and transmit this data to Base Station (BS) and then forward to Medical Server (MS). BSN is exposed to security threats due to vulnerable wireless channel. Protection of human physiological data against adversaries is a major addressable issue while keeping constrained resources of BSN under consideration. Our proposed scheme consists of three stages. In first stage deployment of initial secret key by the ward Medical Officer (MO), in second stage secure key exchange and node authentication, in third stage secure data communication are performed. We have compared our proposed scheme with three existing schemes. Our scheme is efficient in computation cost, communication overhead and storage as compared to existing schemes while providing enough security against the adversaries.

Wireless body area networks (WBANs) are valuable solutions for healthcare lifestyle monitoring applications which allow the continuous screening of health data and constant access to patients despite their current locality or activity, with a fraction of the cost of regular face-to-face examination. In such environments, entities are equipped with intelligence-embedded devices to collect data for providing pervasive information. WBANs can serve as passing reference for huge audience instance systems for architects, practitioners, developers, medical engineers, etc. In particular for the medical field, devices fixed inside the human body measure and transfer real-time data to the caregiver through the communication network. Many technologies have showed their efficiency in secondary WBANs application such as biofeedback, remote sensing, and QoS requirement. This chapter highlights the major applications, design, and security of WBAN.

The significance of the cloud enables the data owners (DOs) to store data remotely in cloud server (CS). The external and internal attacks on the stored data at CS can deliberately remove data. Furthermore, the CS removes the stored data to make empty location for the user's upcoming new data. However, it is a legal expectation of DOs to know whether their data are correctly stored or altered in CS. In this article, we propose a novel privacy-aware and hash-parity-bits-based public auditing (PA-HPPA) framework to secure full data, left half of the data, and the right half of the data, generated by a DO. DO generates two private key pairs with the assistance of a virtual key and a user ID (IP). The virtual key is the sequence number of DO who is registered and provided by the trusted data manager (TDM) while IP is the sequence number of DO working in an organization. Subsequently, DO blinds the categorized data and generates their signatures and hashes. In addition, DO generates the parity bits using xor and assigns to each hard drive (HD) in CS, which assistants to TDM in public auditing. Second, how to identify the error in the stored data and how to securely recover the error/missed data? Extension to the framework, the novel proposed data error identification and secure data recovery produce tags for installed HDs of CS using truth table and recover the altered/missed data via a authenticator, which is produced using xor function. Third, how to protect a valid user from revocation and, in case a user has revoked on merit basis, then how to securely access the stored data of it? This novel work has proposed three conditions to meet the validity of the valid user from revocation and securely generating the public-private key pairs to access the stored data of the revoked user securely from CS. Fourth, there is an efficient novel proposed dynamic operation scheme to insert, update, or delete the stored data at CS without regenerating the signatures, hashes, and tags for the whole stored data in cloud. The security analysis and the performance evaluation of the proposed solutions are provably efficient and secure with reduced communication costs.

Wireless body area networks (WBANs), as a promising health-care system, can provide tremendous benefits for timely and continuous patient care and remote health monitoring. Owing to the restriction of communication, computation and power in WBANs, cloud-assisted WBANs, which offer more reliable, intelligent, and timely health-care services for mobile users and patients, are receiving increasing attention. However, how to aggregate the health data multifunctionally and efficiently is still an open issue to the cloud server (CS). In this paper, we propose a privacy-preserving and multifunctional health data aggregation (PPM-HDA) mechanism with fault tolerance for cloud-assisted WBANs. With PPM-HDA, the CS can compute multiple statistical functions of users’ health data in a privacy-preserving way to offer various services. In particular, we first propose a multifunctional health data additive aggregation scheme (MHDA+) to support additive aggregate functions, such as average and variance. Then, we put forward MHDA $^{\oplus }$ as an extension of MHDA+ to support nonadditive aggregations, such as min/max, median, percentile, and histogram. The PPM-HDA can resist differential attacks, which most existing data aggregation schemes suffer from. The security analysis shows that the PPM-HDA can protect users’ privacy against many threats. Performance evaluations illustrate that the computational overhead of MHDA+ is significantly reduced with the assistance of CSs. Our MHDA $^{\oplus }$ scheme is more efficient than previously reported min/max aggregation schemes in terms of communication overhead when the applications require large plaintext space and highly accurate data.

Cloud computing has providing unlimited storage space to store data in cloud. Service providers can increase space in cloud by incorporating with de-duplication systems. In the previous de-duplication systems, they consider single server to store data in cloud. However, as lots of de-duplication system and storage are intend by users and applications for higher reliability and the data are critical. Hence in this system, the concept of de-duplication and security is achieved by means of Proof of Ownership of the File[POF]. We used multiple server to store the data in cloud in this system. We outsource the keys to slave machine securely in order to keep the data very secure. De-key supports both file level and block level de-duplications. The overhead of key management is avoided in this system and provides fault tolerance for key management. In this system, Triple DES technique is used, as the plain text is encrypted triple times with the convergent keys so that the data will be more secured. Scalability increases as de-key achieved efficiently. Cost efficiency is achieved as multiple users of same data is just referred and not newly added. Deleting content of shared file of different user will allow deleting only convergent key reference not co0ntent stored in Hadoop Distributed File system Storage[HDFS].

Confidentiality is used on accessing the set of cloud database information with high security level. The conventional encryption and decryption mechanism for the privacy maintenance in cloud zone acquire additional processing time. Hence, the cloud service did not explore effective confidentiality on information retrieval process. A new key distribution scheme for efficient privacy preserving query plans on the cloud data achieves a higher percentage of confidentiality by residing the cloud data with polynomial interpolation. To ensure high confidentiality and providing privacy to the cloud users the Shamir's Key Distribution based Confidentiality (SKDC) scheme is employed. SKDC scheme generates a polynomial of degree with the secret as the first coefficient and the remaining coefficients picked up at random to improve the privacy preserving level on the cloud infrastructure. Shamir's Key Distribution supports batch auditing where multiple user requests for data auditing is held concurrently at a higher confidentiality rate. SKDC scheme handles query processing using the matrix-structure form. An Experimental evaluation is performed with Amazon Simple Storage Service dataset to evaluate the confidentiality and privacy performance with Shamir's Key Distribution based Confidentiality (SKDC) scheme is compared with two well-known privacy schemes such as Trusted Third Party Model (TTPM) and Trusted Hardware Based Database. As a result, the SKDC scheme increases confidentiality level of the user by 22 % as compared to existing TTPM method.

Nowadays, cloud services are used by numerous people all around the globe. One of its major applications is in the field of cloud storage. Users can store data in cloud without the need to have har...

In trusted public cloud, allowing data consumers to recover the information with full integrity. The most of research efforts were focused on a single data owner for safe cloud-based encryption of encrypted data. Additionally, searchable encryption makes it possible for a data user to locate a specific encrypted document from a cloud of encrypted data by searching for keywords (KS). This study isn't useful for retrieving relevant results from keyword searches. Such issues may be overcome using the secure and privacy-preserving keyword search retrieval (SPKSR) technology, which allows users to recover hashed encrypted files from cloud data that is also hashed encrypted. Three distinct entities are included in the proposed system: (a) cloud server (CS), (b) data owner (DO), and (c) data users (DU). It is the CS that generates a searchable index tree for the owner’s hashed encrypted files. The CS keeps the index tree structure and the hashed encrypted document set in its own storage. The encrypted data is hashed and DU executes the “search” over it. Analyses and comparisons of the experimental findings of the suggested system with the other existing system demonstrate its superiority.KeywordsCloud serverCloud computingData usersData ownerIndex treeKeyword‐based searchHashed encrypted document

It has been found that the Cloud computing facilities have been used by users of various categories to meet their flexible computing needs which include Processing Powers, Storages, Servers, etc., and among these needs, Public Cloud storage has been used for storing huge data files of different formats and different sizes in Cloud. Because of data security concerns in Cloud, symmetric algorithms have been preferred for encryption and decryption of these data files. Because of limited Internet speed, we must have provision to identify fast and efficient symmetric algorithms for Cloud storages. In the literature survey, we have not found any framework to investigate the efficiency of symmetric algorithms in securing and storing data in Cloud based on various parameters like Encryption Time, Decryption Time, and Throughput, etc.

The current research on still image recognition has been very successful, but the study of action recognition for video classes is still a challenging topic. In this work, we propose a random projection‐based human action recognition algorithm to address the lack of depth information in color information (RGB video frames) that is not easily affected by environmental factors such as illumination and the lack of ability to recognize actions along the direction of view. A network structure is designed to take the obvious advantage of long‐ and short‐term memory networks for controlling and remembering long sequences of historical information. The network structure in this paper is constituted by multiple memory units. At the same time, this paper constructs the spatial features, temporal features, and depth features of the three recognition stream outputs into a feature matrix, whose feature matrix is divided into multiple temporal segments according to the temporal dimension, then inputs them into the network layer in order, and achieves the fusion of the feature matrix in this paper according to their correlation characteristics on the temporal axis. Here, we proposed the concept of random batch projection operators. This basically uses as much sublimitation information as possible to improve projection accuracy by randomly selecting several subdependencies as projections defined during projection. A compressed sensing design of human motion acceleration data for low‐power body area networks is proposed, and the basic idea and implementation process of compressed sensing theory for human motion data compression and reconstruction in wireless body area networks are introduced in detail.

Cloud computing technology and services has become an important issue in the recent years. Cloud Computing needs lot of attention, time and innovative concepts for the technology to mature over a period of time. Many organizations show interest in adopting cloud computing technology and services because of its socio economic and time factor. Cloud computing is nothing but virtual centralization of different computers where the software and memory space is provided by the vendor and the data is managed by them. This leaves the client/customer unaware of where the process is running or where the data is stored. Security of the data is highly dependent on the vendor who has to provide an assurance to the customer on security issues by making service level agreements. This paper focus on security issues, requirements for providing a secured data in cloud computing environment by giving a standard service oriented cloud computing Architecture and management tools that can be used by the organizations for authentication, confidentiality and integrity. Thus providing secured data access and transfer.