Raghavendra S

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.

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.

RSSMSO Rapid Similarity Search on Metric Space Object Stored in Cloud Environment

This paper involves a cloud computing environment in which the dataowner outsource the similarity search service to a third party service provider. Privacy of the outsourced data is important because they may be confidential data. The data should be made available to the authorized client groups, but not to be revealed to the service provider in which the data is stored. Given this scenario, the paper presents a technique called RSSMSO which has build phase, query phase, data transformation and search phase. The build phase and the query phase are about uploading the data and querying the data respectively; the data transformation phase transforms the data before submitting it to the service provider for similarity queries on the transformed data; search phase involves searching similar object with respect to query object. The RSSMSO technique provides enhanced query accuracy with low communication cost. Experiments have been carried out on real data sets which exhibits that the proposed work is capable of providing privacy and achieving accuracy at a low cost in comparison with FDH

Geospatial Data for the Himalayan Region: Requirements, Availability, and Challenges

Availability of appropriate geospatial data over the Himalayan and adjacent Tibetan Plateau region has emerged as one of the key data requirements to understand the fragile landscape dynamics, changing climate and its implications, and assessment of natural resources of the region. In the Himalayas, there are three principal agents of change: plate tectonics, climate change, and human interaction; all three work in a very intricate manner to modulate all natural processes and features. Data requirements can be as diverse as the processes and features of the Himalayas. Spatial resolution requirements of RS data vary from tens of centimeters to tens of meters and a few minutes to years in terms of temporal resolution. Availability of data was limited to systematic aerial photography by the mapping agencies carried out in the second half of the last century and resultant topographical maps on 1:50,000 scale earlier. Satellite coverages are available from the early 1970s. In spite of enormous progress in satellite imaging, large tracts of Himalaya remain unexplored in terms of data availability at a high spatial resolution. Therefore, in spite of wide spread concern, development needs, and environment and security issues, the Himalayas have emerged as “data-scarce” region of world. In this context, it is very pertinent to evaluate existing datasets, and then a systematic attempt can be made on data acquisition strategy involving near-ground (UAVs (unmanned aerial vehicles)) platforms to aerial and spacecraft platforms. The spatial data acquisition strategy should be driven by the most immediate concerns of the region, i.e., disaster monitoring and mitigation, natural resource management including cryosphere status vis-a-vis climate change impact assessment, infrastructure development, and crustal deformation.

Cost and fault-tolerant aware resource management for scientific workflows using hybrid instances on clouds

Cloud service providers are offering computing resources at a reasonable price as a pay-per-use model. Further, cloud service providers have also introduced different pricing models like spot, blockspot and spotfleet instances that are cost effective and user’s have to go through the bidding to balance the reliability and monetary costs. Henceforth, Scientific Workflows (SWf) that are used to model applications of high throughput, computation and complex large-scale data analysis are significantly adopting these computing resources. Nevertheless, spot instances are terminated when the market spot price exceeds the users bid price. Moreover, failures are inevitable in such a large distributed systems and often pose a challenge to design a fault-tolerant scheduling algorithm for SWf. This paper presents an efficient, low-cost and fault-tolerant scheduling algorithm and a bidding strategy to minimize the volatility and cost of resource provisioning for SWf. The proposed algorithm uses spot and blockspot instances as hybrid instances in comparison with on-demand instance to reduce the execution cost and fault-tolerant while meeting the SWf deadline. The results obtained reveal the promising potential of the proposed scheduling algorithm and are demonstrated through empirical simulation study that is robust under short deadlines with minimal makespan and cost.

Split keyword fuzzy and synonym search over encrypted cloud data

A substitute solution for various organizations of data owners to store their data in the cloud using storage as a service(SaaS). The outsourced sensitive data is encrypted before uploading into the cloud to achieve data privacy. The encrypted data is search based on keywords and retrieve interested files by data user using a lot of traditional Search scheme. Existing search schemes supports exact keyword match or fuzzy keyword search, but synonym based multi-keyword search are not supported. In the real world scenario, cloud users may not know the exact keyword for searching and they might give synonym of the keyword as the input for search instead of exact or fuzzy keyword due to lack of appropriate knowledge of data. In this paper, we describe an efficient search approach for encrypted data called as Split Keyword Fuzzy and Synonym Search (SKFS). Multi-keyword ranked search with accurate keyword and Fuzzy search supports synonym queries are a major contribution of SKFS. The wildcard Technique is used to store the keywords securely within the index tree. Index tree helps to search faster, accurate and low storage cost. Extensive experimental results on real-time data sets shows, the proposed solution is effective and efficient for multi-keyword ranked search and synonym queries Fuzzy based search over encrypted cloud data.

Emerging trends, issues and challenges in Internet of Things, Big Data and cloud computing

Abstract Although Big Data, IoT and cloud computing are three distinct approaches that have evolved independently, they are becoming more and more interconnected over time. The convergence of IoT, Big Data and clouds provides new opportunities and results in development of new applications in many fields, including business, healthcare, sciences and engineering. At the same time, various challenges are faced during processing and management of massive amounts of data, as well as during their storage in cloud environments. This special issue presents novel research approaches related to Big Data, IOT and cloud computing. It also discusses the encountered problems and open issues.

An Analysis of Leg Muscle Stretch Using 3D Digital Image Correlation

Suralregioniscalledasthebottomheartofthebodysinceitisessentialforthemaintenanceofvenous circulatoryadequacyduringuprightpostureandactivity.Previousresearchhasfoundthatanklejoint equinuscanleadtofootpathologies.Thepaperpresentsagenericmethodologytocomputethestrain patternintheSuralandcalcanealregionduringlegdorsiflexionexperiment.Intheexperiment,the subjectismadetostandonaninclinationplaneandimagesarecapturedatvaryingangularinclinations. Strainplotsobtainedaftercomparisonindicatesthestraindistributionintheposteriorcompartment ofsuralandcalcanealregions.Theexperimentisthenrepeatedforfourotherparticipantsandthe trendsareobserved.VIC-3Disusedtodeterminethestraindistributionontwoimportantsuperficial componentsofthelegregion,namelytheSuralandcalcanealregions,subjectedtovarieddegrees offootdorsiflexion.Theexperimentisextremelyimportantastheprimaryknowledgegainedwill assistsustogeneratemuscle-tendonunitswhichcanresultintobetterunderstandingoftheforceand energyproduction.Moreover,thisexercisecanbeusedtoregulatethebloodcirculationandavoid thesyndromementionedabove. KeywORDS Calcaneal Region, Digital Image Correlation (DIC), Sural Region, VIC 3D

FRORSS: Fast result object retrieval using similarity search on cloud

This paper involves a cloud computing environment in which the data owner out sources the similarity search service to a third party service provider. The user provides an example query to the server to retrieve similar data. Privacy of the outsourced data is important because they may be sensitive, valuable or confidential data. The data should be made available to the authorized client/client groups, but not to be revealed to the service provider in which the data is stored. Given this scenario, the paper presents a technique called FRORSS which has build phase, data transformation and search phase. The build phase is about uploading the data; the data transformation phase transforms the data before submitting it to the service provider for similarity queries on the transformed data; search phase involves searching similar object with respect to query object. Experiments have been carried out on real data sets which exhibits that the proposed work is capable of providing privacy and achieving accuracy at a lower value of result measure in comparision with FDH [1].