IGSK: Index Generation on Split Keyword for search over cloud data

Storage as a Service (Saas) of cloud computing has become an alternative option for data owners of various organizations to store their data into the cloud. Usually sensitive data is encrypted to achieve data security and then it is outsourced into cloud. Many traditional search schemes allow data user to search over encrypted cloud data through keywords and retrieve the files of interest selectively. In this paper, we propose an efficient approach for keyword search over encrypted cloud data. The main contribution of this paper involves index generation method for keywords by using split factor. The keywords are stored in wildcard based technique within the index tree that is stored securely with low storage cost. Extensive experimental results on real-time data sets shows that the proposed solution is effective as well as efficient in Index generation and storage cost

DRSIG: Domain and Range Specific Index Generation for encrypted Cloud data

One of the most fundamental services of cloud computing is Cloud storage service. Huge amount of sensitive data is stored in the cloud for easy remote access and to reduce the cost of storage. The confidential data is encrypt before uploading to the cloud server in order to maintain privacy and security. All conventional searchable symmetric encryption(SSE) schemes enable the users to search on the entire index file. In this paper, we propose the Domain and Range Specific Index Generation(DRSIG) scheme that minimizes the Index Generation time. This scheme adopts collection sort technique to split the index file into D Domains and R Ranges. The Domain is based on the length of the keyword; the Range splits within the domain based on the first letter of the keyword. A mathematical model is used to encrypt the indexed keyword that eliminates the information leakage. The time complexity of the index generation is O(NT × 3) where NT - Number of rows in index document and 3 is Number of columns in index document. Experiments have been conducted on real world dataset to validate proposed DRSIG scheme. It is observed that DRSIG scheme is efficient and provide more secure data than Ranked Searchable Symmetric Encryption(RSSE) Scheme

MSIGT: Most Significant Index Generation Technique for cloud environment

Cloud Computing is a computing paradigm for delivering computational power, storage and applications as services via Internet on a pay-as-you-go basis to consumers. The data owner outsources local data to the public cloud server to reduce the cost of the data management. Critical data has to be encrypted to ensure privacy before outsourcing. The state-of-the-art SSE schemes search only over encrypted data through keywords, hence they do not provide effective data utilisation for large dataset files in cloud. We propose a Most Significant Index Generation Technique (MSIGT), that supports secure and efficient index generation time using a Most Significant Digit (MSD) radix sort. MSD radix sort is simple and faster in sorting array strings. A mathematical model is developed to encrypt the indexed keywords for secure index generation without the overhead of learning from the attacker/cloud provider. It is seen that the MSIGT scheme can reduce the cost of data on owner side to O(NT × 3) with a score calculation of O(NT). The proposed scheme is effective and efficient in comparison with the existing algorithms

Index Generation and Secure Multi-User Access Control over an Encrypted Cloud Data

Cloud computing provides economical and effective solution for sharing data among cloud users with low maintenance cost. The security of data and identity confidentiality while sharing data in multi-owner way cannot be assured by the Cloud Service Providers (CSP’s). The Cloud Service Providers are reliable but curious to know the recurrent membership changes in the cloud. In this paper,we propose a secure multi-owner data sharing for dynamic group in the cloud with RSA Chinese Remainder Theorem (RSA-CRT)encryption technique and substring index generation method. RSA-CRT efficiently manages revocation list, key management, with reduced storage and computational overhead. The substring Index generation algorithm reduces the storage space compared to wild card fuzzy alogorithm1

Dynamic management of traffic signals through social IoT

Traffic congestion is a major threat to transportation sector in every urban city around the world. This causes many adverse effects like, heavy fuel consumption, increased waiting time, pollution, etc. and pose an eminent challenge to the movement of emergency vehicles. To achieve better driving we proceed towards a trending research field called Social Internet of Vehicles (SIoV). A social network paradigm that permits the establishment of social relationships among every vehicle in the network or with any road infrastructure can be radically helpful. This holds as the aim of SIoV, to be beneficial for the drivers, in improving the road safety, avoiding mishaps, and have a friendly-driving environment. In this paper, we propose a Dynamic congestion control with Throughput Maximization scheme based on Social Aspect (D-TMSA) utilizing the social, behavioral and preference-based relationships. Our proposed

MSSS: most significant single-keyword search over encrypted cloud data

Cloud Computing is a popular computing technique via the Internet. The data owner outsources local data to the public cloud server to reduce the cost of the data management. Sensitive data has to be encrypted to ensure privacy before outsourcing. All traditional Searchable Symmetric Encryption (SSE) schemes search only over encrypted data through keywords,hence they do not provide effective data utilisation for large dataset files in cloud. In this paper, we propose a Most Significant Single-keyword Search (MSSS), that supports efficient search using a Most Significant Digit (MSD) radix sort. MSD radix sort is simple and faster in sorting array strings. A mathematical model is developed to encrypt the indexed keywords for secure search without the overhead of learning from the attacker/cloud provider. The proposed scheme reduces the computation overhead. Through numerical analysis, it is shown that the MSSS scheme can reduce the computation cost of data on owner side to O(NT × 3). The time complexity of search time is reduced to O(B) for bucket size B. The proposed scheme is highly secure and efficient in comparison to the state of the art work

Context-oriented user-centric search system for the IoT based on fuzzy clustering

The Internet of Things (IoT) paradigm envisions to support the creation of several applications that aids in the betterment of the society from various sectors such as environment, finance, industry etc. These applications are to be user-centric for their larger acceptance by the society. With the increase in the number of sensors that should are getting connected to the IoT infrastructure, there is an augmented increase in the amount of data generated by these sensors. Therefore it becomes a fundamental requirement to search for the sensors that produce the most applicable data required by the application. In this regard, context parameters of the sensors and the application users can be utilized to effectively filter out sensors from a large group. This paper proposes a sensor search scheme based on semantic-weights and fuzzy clustering. We have modified the traditional fuzzy c-means clustering algorithm by

Searching for the IoT resources: fundamentals, requirements, comprehensive review, and future directions

Internet of Things (IoT) paradigm links physical objects in the real world to cyber world and enables the creation of smart environments and applications. A physical object is the fundamental building block of the IoT, known as a Smart Device, that can monitor the environment. These devices can communicate with each other and have data processing abilities. When deployed, smart devices collect real-time data and publish the gathered data on the Web. The functionality of smart devices can be abstracted as a service and an IoT application can be built by combining the smart devices with these services that help to address challenges of day-to-day activities. The IoT comprises billions of these intelligent communicating devices that generate enormous amount of data, and hence performing analysis on this data is a significant task. Using search techniques, the size and extent of data can be reduced and limited, so that an application can choose just the most important and valuable data items as per its necessities. It is, however, a tedious task to effectively seek and select a proper device and/or its data among a large number of available devices for a specific application. Search techniques are fundamental to IoT and poses various challenges like a large number of devices, dynamic availability, restrictions on resource utilization, real time data in various types and formats, past and historical monitoring. In the recent past, various methods and techniques have been developed by the research community to address these issues. In this paper, we present a review of the state-of-the-art search methods for the IoT, classifying them according to their design principle and search approaches as: IoT data and IoT object-based techniques. Under each classification, we describe the method adopted, their advantages and disadvantages. Finally, we identify and discuss key challenges and future research directions that will allow the next generation search techniques to recognize..

Drcm: Dynamic Relationship Creation and Management in Social Internet of Things

As internet of things (IoT) is overpopulated with a multitude of objects, services and interactions locating the most relevant object is emerging as a major obstacle. Over the last few years, the social internet of things (SIoT) paradigm, where objects independently establish social relationships with the other things has become popular as it provides several new characteristics to carryout reliable discovery approaches. Given a large scale deployment of socially connected objects, finding the shortest path to reach the service provider remains as a fundamental challenge. Most of the existing techniques, search for a specific object or service utilising its friendship or friends of friends connections. As a result, each object has to manage a large set of friends, thus slowing down the search process. In this paper, we propose a similarity based object search mechanism that dynamically creates and manages relationships based on physical location proximity and social context of users in social communities. The result shows enhancement in the proposed method over the existing search techniques FSS, FSASV and LSFGA in terms of local cluster coefficients, the average degree of connections and average path length