JPJ1404 Building Confidential And Efficient Query Services In The Cloud Wit...chennaijp
We are good ieee java projects development center in chennai and pondicherry. We guided advanced java techonolgies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
Building confidential and efficient query services in the cloud with rasp dat...LeMeniz Infotech
Building confidential and efficient query services in the cloud with rasp data perturbation
With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. Security.
Building confidential and efficient query services in the cloud with rasp dat...Adz91 Digital Ads Pvt Ltd
The document proposes the RASP (Random Space Perturbation) approach to build confidential and efficient range and k-nearest neighbor (kNN) query services in the cloud. RASP combines order preserving encryption, dimensionality expansion, random noise injection, and random projection to securely transform data while preserving ranges for efficient query processing. It defines the RASP perturbation method and constructs private range and kNN query services. Experimental results demonstrate advantages in efficiency and security.
Storage for big-data by Joshua RobinsonData Con LA
Abstract:- Headaches dealing with big-data storage? A Pure Engineering use case: See how we use Spark and other big data technologies to improve our software engineering development cycle through automated testing and automated failure triaging. Learn how we gain flexibility, agility, and simplicity by leveraging the FlashBlade as a shared storage system across multiple silos of infrastructure instead of distributed DAS deployments.
Spark + Flashblade: Spark Summit East talk by Brian GoldSpark Summit
Modern infrastructure and applications generate extraordinary volumes of log and telemetry data. At Pure Storage, we know this first hand: we have over 5PB of log data from production customers running our all-flash storage systems, from our engineering testbeds, and from test stations at manufacturing partners. Every part of our company — from engineering to sales — now depends on the insights we gather from this data. Given the diversity of our end users, it’s no surprise that our analysis tools comprise a broad mix of reporting queries, stream-processing operations, ad-hoc analyses, and deeper machine-learning algorithms. In this session, we will cover lessons learned from scaling our data warehouse and how we are leveraging Apache Spark’s capabilities as a central hub to meet our analytics demands.
The document discusses the growing challenges of analyzing large and diverse datasets. It notes that while data stores are growing exponentially, existing systems are limited and cannot scale to handle petabytes of data. Only 10-20% of data is currently analyzed, leaving valuable insights undiscovered. The document then introduces SQream DB as a massively parallel GPU-accelerated database that can analyze terabytes to petabytes of data using ANSI SQL. It claims SQream DB on IBM Power9 systems with NVLink can load data twice as fast and query performance 3.7x faster than comparable x86 systems, thanks to the high-throughput architecture of Power9 and GPU connectivity.
Adam Fuchs' presentation slides on what's next in the evolution of BigTable implementations (transactions, indexing, etc.) and what these advances could mean for the massive database that gave rise to Google.
JPJ1404 Building Confidential And Efficient Query Services In The Cloud Wit...chennaijp
We are good ieee java projects development center in chennai and pondicherry. We guided advanced java techonolgies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
Building confidential and efficient query services in the cloud with rasp dat...LeMeniz Infotech
Building confidential and efficient query services in the cloud with rasp data perturbation
With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. Security.
Building confidential and efficient query services in the cloud with rasp dat...Adz91 Digital Ads Pvt Ltd
The document proposes the RASP (Random Space Perturbation) approach to build confidential and efficient range and k-nearest neighbor (kNN) query services in the cloud. RASP combines order preserving encryption, dimensionality expansion, random noise injection, and random projection to securely transform data while preserving ranges for efficient query processing. It defines the RASP perturbation method and constructs private range and kNN query services. Experimental results demonstrate advantages in efficiency and security.
Storage for big-data by Joshua RobinsonData Con LA
Abstract:- Headaches dealing with big-data storage? A Pure Engineering use case: See how we use Spark and other big data technologies to improve our software engineering development cycle through automated testing and automated failure triaging. Learn how we gain flexibility, agility, and simplicity by leveraging the FlashBlade as a shared storage system across multiple silos of infrastructure instead of distributed DAS deployments.
Spark + Flashblade: Spark Summit East talk by Brian GoldSpark Summit
Modern infrastructure and applications generate extraordinary volumes of log and telemetry data. At Pure Storage, we know this first hand: we have over 5PB of log data from production customers running our all-flash storage systems, from our engineering testbeds, and from test stations at manufacturing partners. Every part of our company — from engineering to sales — now depends on the insights we gather from this data. Given the diversity of our end users, it’s no surprise that our analysis tools comprise a broad mix of reporting queries, stream-processing operations, ad-hoc analyses, and deeper machine-learning algorithms. In this session, we will cover lessons learned from scaling our data warehouse and how we are leveraging Apache Spark’s capabilities as a central hub to meet our analytics demands.
The document discusses the growing challenges of analyzing large and diverse datasets. It notes that while data stores are growing exponentially, existing systems are limited and cannot scale to handle petabytes of data. Only 10-20% of data is currently analyzed, leaving valuable insights undiscovered. The document then introduces SQream DB as a massively parallel GPU-accelerated database that can analyze terabytes to petabytes of data using ANSI SQL. It claims SQream DB on IBM Power9 systems with NVLink can load data twice as fast and query performance 3.7x faster than comparable x86 systems, thanks to the high-throughput architecture of Power9 and GPU connectivity.
Adam Fuchs' presentation slides on what's next in the evolution of BigTable implementations (transactions, indexing, etc.) and what these advances could mean for the massive database that gave rise to Google.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
In this training session, two leading security experts review how adversaries use DNS to achieve their mission, how to use DNS data as a starting point for launching an investigation, the data science behind automated detection of DNS-based malicious techniques and how DNS tunneling and DGA machine learning algorithms work.
Watch the presentation with audio here: http://info.sqrrl.com/leveraging-dns-for-proactive-investigations
October 2014 Webinar: Cybersecurity Threat DetectionSqrrl
Using Sqrrl Enterprise and the GraphX library included in Apache Spark, we will construct a dynamic graph of entities and relationships that will allow us to build baseline patterns of normalcy, flag anomalies on the fly, analyze the context of an event, and ultimately identify and protect against emergent cyber threats.
This document discusses performance optimization of Apache Accumulo, a distributed key-value store. It describes modeling Accumulo's bulk ingest process to identify bottlenecks, such as disk utilization during the reduce phase. Optimization efforts included improving data serialization to speed sorting, avoiding premature data expansion, and leveraging compression. These techniques achieved a 6x speedup. Current Accumulo performance projects include optimizing metadata operations and write-ahead log performance.
This document discusses the status of big data adoption and highlights of big data technologies. It provides an overview of Hadoop and its ecosystem, along with common cluster sizes and topologies. It then discusses technologies like HBase, HDFS, Parquet, and Kudu for structured data storage and processing. Benchmarks show Kudu outperforming HBase and Parquet in various operations. The document also describes a near-real-time data lake solution built using Zwoox for ingestion and Kafka as a message bus. Emerging trends of IoT and data science are briefly mentioned.
Analyzing 1.2 Million Network Packets per Second in Real-timeDataWorks Summit
The document describes Cisco's OpenSOC, an open source security operations center that can analyze 1.2 million network packets per second in real time. It discusses the business need for such a solution given how breaches often go undetected for months. The solution architecture utilizes big data technologies like Hadoop, Kafka and Storm to enable real-time processing of streaming data at large scale. It also provides lessons learned around optimizing the performance of components like Kafka, HBase and Storm topologies.
This document discusses Trend Micro's experience scaling their big data infrastructure for threat detection. It describes how their infrastructure and data needs have grown substantially over time. Trend Micro now processes over 8 billion URLs and collects over 7 TB of data daily from a global network of over 3 billion sensors using Hadoop clusters. They have also developed machine learning and data mining techniques to analyze this data and identify threats, allowing them to block malicious URLs and threats within 15 minutes of appearing online. The document outlines lessons learned around scaling infrastructure to handle unstructured and high-volume data streams for timely cyber threat analysis.
Real time big data applications with hadoop ecosystemChris Huang
The document discusses real-time big data applications using the Hadoop ecosystem. It provides examples of operational and analytical use cases for online music and banking. It also discusses technologies like Impala, Stinger, Kafka and Storm that can enable near real-time and interactive analytics. The key takeaways are that real-time does not always mean faster than batch, and that a combination of batch and real-time processing is often needed to build big data applications.
IBM Aspera in Chemical & Petroleum InfographicChris Shaw
IBM Aspera solutions can help chemical and petroleum companies overcome challenges in collaborating and transferring large data sets globally. Specifically, it allows for faster and more efficient exploration and production by maximizing data transfer speeds from remote sensor locations. It also improves refining and manufacturing efficiency by easily handling large file sizes and meeting deadlines. Additionally, Aspera can automatically back up, synchronize, recover, and distribute files to increase dependability over traditional technologies. The chemical and petroleum industry currently lags behind in developing digital strategies for global collaboration and data transfer compared to other industries.
Scaling Your Skillset with Your Data with Jarrett Garcia (Nielsen)Spark Summit
More data is being created than ever before and the rate at which it is being created is not slowing. This means that new techniques are being developed and used to tackle big data challenges. At Nielsen, big data means opportunities for new products like digital measurement, but also exposed a skills gap for dealing with big data. This session will cover Nielsen’s successful implementation of Spark and Databricks which has allowed Nielsen to scale its products and its Data Scientists’ skillsets.
The document proposes a method called RAndom Space Perturbation (RASP) to provide secure and efficient range and k-nearest neighbor (kNN) query services for protected data hosted in the cloud. RASP combines order preserving encryption, dimensionality expansion, random noise injection, and random projection to transform data in a way that preserves the topology of multidimensional ranges, allowing for efficient query processing while providing strong confidentiality guarantees. The authors analyze attacks on the RASP-protected data and queries under a defined threat model and security assumptions. Experimental results demonstrate advantages of the RASP approach in efficiency and security for cloud-based query services.
Literature Survey on Buliding Confidential and Efficient Query Processing Usi...paperpublications3
Abstract: Hosting data query services with the deployed cloud computing infrastructure increase the scalability and high performance evaluations with low cost. However, some data owners might not be interested to the save their in the cloud environment because of data confidentiality and query processing privacy should be guaranteed by the cloud service providers. Secured Query should able to provide very high efficient of query processing and also should reduce in – house workload. In this paper we proposed RASP data perturbation techniques combines various objectives like random noise injection, dimensionality expansion, efficient encryption and random projection, henceforth RASP methodology are also used to preserves multidimensional ranges. KNN – R algorithm used to work with RASP range for processing KNN queries. The experimental result of our project carried out to define realistic security and threat model approaches for improved efficient and security.
Enabling Efficient and Geometric Range Query with Access Control over Encrypt...JAYAPRAKASH JPINFOTECH
Enabling Efficient and Geometric Range Query with Access Control over Encrypted Spatial Data
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Enabling Efficient and Geometric Range Query with Access Control over Encrypt...JAYAPRAKASH JPINFOTECH
Enabling Efficient and Geometric Range Query with Access Control over Encrypted Spatial Data
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
This document discusses accelerating cyber threat detection with GPUs. It begins by noting that current detection methods are too slow, taking an average of 98 days for financial services and up to 7 months for retailers. It then discusses how attacks are becoming more sophisticated and provides examples. The document outlines principles for cybersecurity, including improving indication of compromise through combining machine learning, graph analysis, and other methods. It discusses building an anomaly detection platform using deep learning and GPUs for improved performance. It also covers using GPU databases and visualization to further accelerate analytics and hunting of threats.
Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Clou...JAYAPRAKASH JPINFOTECH
Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Cloud Data
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Genomic sequencing is growing at a rate of 100 million sequences a year, which translates into 40 EB by the year 2025. It is daunting to meeting the challenge of handling this level of growth and perform big data analytics. In this session, learn how one genomics company is now able to increase their sequencing capacity by 20x using NetApp Cloud Volumes for AWS. Understand how they can now perform sequencing at an extremely fast rate and complete analysis at a global scale.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFiDataWorks Summit
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
In this training session, two leading security experts review how adversaries use DNS to achieve their mission, how to use DNS data as a starting point for launching an investigation, the data science behind automated detection of DNS-based malicious techniques and how DNS tunneling and DGA machine learning algorithms work.
Watch the presentation with audio here: http://info.sqrrl.com/leveraging-dns-for-proactive-investigations
October 2014 Webinar: Cybersecurity Threat DetectionSqrrl
Using Sqrrl Enterprise and the GraphX library included in Apache Spark, we will construct a dynamic graph of entities and relationships that will allow us to build baseline patterns of normalcy, flag anomalies on the fly, analyze the context of an event, and ultimately identify and protect against emergent cyber threats.
This document discusses performance optimization of Apache Accumulo, a distributed key-value store. It describes modeling Accumulo's bulk ingest process to identify bottlenecks, such as disk utilization during the reduce phase. Optimization efforts included improving data serialization to speed sorting, avoiding premature data expansion, and leveraging compression. These techniques achieved a 6x speedup. Current Accumulo performance projects include optimizing metadata operations and write-ahead log performance.
This document discusses the status of big data adoption and highlights of big data technologies. It provides an overview of Hadoop and its ecosystem, along with common cluster sizes and topologies. It then discusses technologies like HBase, HDFS, Parquet, and Kudu for structured data storage and processing. Benchmarks show Kudu outperforming HBase and Parquet in various operations. The document also describes a near-real-time data lake solution built using Zwoox for ingestion and Kafka as a message bus. Emerging trends of IoT and data science are briefly mentioned.
Analyzing 1.2 Million Network Packets per Second in Real-timeDataWorks Summit
The document describes Cisco's OpenSOC, an open source security operations center that can analyze 1.2 million network packets per second in real time. It discusses the business need for such a solution given how breaches often go undetected for months. The solution architecture utilizes big data technologies like Hadoop, Kafka and Storm to enable real-time processing of streaming data at large scale. It also provides lessons learned around optimizing the performance of components like Kafka, HBase and Storm topologies.
This document discusses Trend Micro's experience scaling their big data infrastructure for threat detection. It describes how their infrastructure and data needs have grown substantially over time. Trend Micro now processes over 8 billion URLs and collects over 7 TB of data daily from a global network of over 3 billion sensors using Hadoop clusters. They have also developed machine learning and data mining techniques to analyze this data and identify threats, allowing them to block malicious URLs and threats within 15 minutes of appearing online. The document outlines lessons learned around scaling infrastructure to handle unstructured and high-volume data streams for timely cyber threat analysis.
Real time big data applications with hadoop ecosystemChris Huang
The document discusses real-time big data applications using the Hadoop ecosystem. It provides examples of operational and analytical use cases for online music and banking. It also discusses technologies like Impala, Stinger, Kafka and Storm that can enable near real-time and interactive analytics. The key takeaways are that real-time does not always mean faster than batch, and that a combination of batch and real-time processing is often needed to build big data applications.
IBM Aspera in Chemical & Petroleum InfographicChris Shaw
IBM Aspera solutions can help chemical and petroleum companies overcome challenges in collaborating and transferring large data sets globally. Specifically, it allows for faster and more efficient exploration and production by maximizing data transfer speeds from remote sensor locations. It also improves refining and manufacturing efficiency by easily handling large file sizes and meeting deadlines. Additionally, Aspera can automatically back up, synchronize, recover, and distribute files to increase dependability over traditional technologies. The chemical and petroleum industry currently lags behind in developing digital strategies for global collaboration and data transfer compared to other industries.
Scaling Your Skillset with Your Data with Jarrett Garcia (Nielsen)Spark Summit
More data is being created than ever before and the rate at which it is being created is not slowing. This means that new techniques are being developed and used to tackle big data challenges. At Nielsen, big data means opportunities for new products like digital measurement, but also exposed a skills gap for dealing with big data. This session will cover Nielsen’s successful implementation of Spark and Databricks which has allowed Nielsen to scale its products and its Data Scientists’ skillsets.
The document proposes a method called RAndom Space Perturbation (RASP) to provide secure and efficient range and k-nearest neighbor (kNN) query services for protected data hosted in the cloud. RASP combines order preserving encryption, dimensionality expansion, random noise injection, and random projection to transform data in a way that preserves the topology of multidimensional ranges, allowing for efficient query processing while providing strong confidentiality guarantees. The authors analyze attacks on the RASP-protected data and queries under a defined threat model and security assumptions. Experimental results demonstrate advantages of the RASP approach in efficiency and security for cloud-based query services.
Literature Survey on Buliding Confidential and Efficient Query Processing Usi...paperpublications3
Abstract: Hosting data query services with the deployed cloud computing infrastructure increase the scalability and high performance evaluations with low cost. However, some data owners might not be interested to the save their in the cloud environment because of data confidentiality and query processing privacy should be guaranteed by the cloud service providers. Secured Query should able to provide very high efficient of query processing and also should reduce in – house workload. In this paper we proposed RASP data perturbation techniques combines various objectives like random noise injection, dimensionality expansion, efficient encryption and random projection, henceforth RASP methodology are also used to preserves multidimensional ranges. KNN – R algorithm used to work with RASP range for processing KNN queries. The experimental result of our project carried out to define realistic security and threat model approaches for improved efficient and security.
Enabling Efficient and Geometric Range Query with Access Control over Encrypt...JAYAPRAKASH JPINFOTECH
Enabling Efficient and Geometric Range Query with Access Control over Encrypted Spatial Data
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Enabling Efficient and Geometric Range Query with Access Control over Encrypt...JAYAPRAKASH JPINFOTECH
Enabling Efficient and Geometric Range Query with Access Control over Encrypted Spatial Data
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
This document discusses accelerating cyber threat detection with GPUs. It begins by noting that current detection methods are too slow, taking an average of 98 days for financial services and up to 7 months for retailers. It then discusses how attacks are becoming more sophisticated and provides examples. The document outlines principles for cybersecurity, including improving indication of compromise through combining machine learning, graph analysis, and other methods. It discusses building an anomaly detection platform using deep learning and GPUs for improved performance. It also covers using GPU databases and visualization to further accelerate analytics and hunting of threats.
Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Clou...JAYAPRAKASH JPINFOTECH
Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Cloud Data
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
Genomic sequencing is growing at a rate of 100 million sequences a year, which translates into 40 EB by the year 2025. It is daunting to meeting the challenge of handling this level of growth and perform big data analytics. In this session, learn how one genomics company is now able to increase their sequencing capacity by 20x using NetApp Cloud Volumes for AWS. Understand how they can now perform sequencing at an extremely fast rate and complete analysis at a global scale.
A Survey on Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encryp...IRJET Journal
This document summarizes a research paper that proposes a secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. It presents an index structure using a greedy depth-first search and a KNN algorithm to encrypt the index and queries. This allows calculating relevance scores between encrypted indexes and queries without decrypting data. The scheme supports accurate multi-keyword searches and flexible dynamic operations like updates and deletions on the document collection. Vector space models and TF-IDF are used to represent documents. Related works on searchable encryption, homomorphic encryption, and fuzzy keyword search techniques are also summarized.
Parallel and distributed system projects for java and dot netredpel dot com
This document contains summaries of 8 papers related to distributed systems, cloud computing, and information security. The papers cover topics such as stochastic modeling of data center performance in cloud systems, cost-effective privacy preservation of intermediate data sets in the cloud, capacity of data collection in wireless sensor networks, denial-of-service attack detection using multivariate correlation analysis, dynamic resource allocation using virtual machines in cloud computing, secure outsourcing of large-scale systems of linear equations to the cloud, and deanonymization attacks against anonymized social networks.
SECURE & EFFICIENT AUDIT SERVICE OUTSOURCING FOR DATA INTEGRITY IN CLOUDSGyan Prakash
Cloud-based outsourced storage relieves the client’s load for storage management and maintenance by providing a comparably low-cost, scalable, location-independent platform. Though, the information that clients no longer have physical control of data specifies that they are facing a potentially formidable risk for missing or corrupted data. To avoid the security risks, inspection services are serious to ensure the integrity and availability of outsourced data and to achieve digital forensics and reliability on cloud computing. Provable data possession (PDP), which is a cryptographic method for validating the reliability of data without retrieving it at an untrusted server, can be used to realize audit services. In this project, profiting from the interactive zero-knowledge proof system, the construction of an interactive PDP protocol to prevent the fraudulence of prover (soundness property) and the leakage of verified data (zero knowledge property).To prove that our construction holds these properties based on the computation Diffie–Hellman assumption and the rewindable black-box knowledge extractor. An efficient mechanism on probabilistic queries and periodic verification is proposed to reduce the audit costs per verification and implement abnormal detection timely. Also, we present an efficient method for choosing an optimal parameter value to reduce computational overheads of cloud audit services.
Enabling fine grained multi-keyword search supporting classified sub-dictiona...finalsemprojects
This document proposes a scheme for enabling fine-grained multi-keyword search over encrypted cloud data. It introduces relevance scores and preference factors for keywords to improve search functionality and user experience. The scheme supports complex logic searches using mixed "AND", "OR", and "NO" keyword operations. It also employs classified sub-dictionaries to improve index building, trapdoor generation, and query efficiency. The scheme aims to address limitations of existing systems in supporting personalized search with comprehensive logic operations over encrypted data.
IRJET - Efficient and Verifiable Queries over Encrypted Data in CloudIRJET Journal
This document proposes a scheme for efficient and verifiable queries over encrypted data stored in the cloud. It aims to allow an authorized user to query encrypted documents of interest while maintaining privacy. The scheme provides a verification mechanism to allow users to check the correctness of query results and identify any valid results omitted by a potentially untrustworthy cloud server. The document reviews related work on searchable encryption and verifiable queries. It then outlines the proposed approach to build secure verifiable queries for encrypted cloud data.
web service recommendation via exploiting location and qo s informationswathi78
This document proposes a novel collaborative filtering-based web service recommender system to help users select services with optimal quality of service (QoS) performance. The recommender system employs location information and QoS values to cluster users and services, and makes personalized recommendations. It achieves considerable improvement in recommendation accuracy compared to existing methods. Comprehensive experiments using over 1.5 million QoS records from real-world web services demonstrate the effectiveness of the approach.
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
This study Examines the Effectiveness of Talent Procurement through the Imple...DharmaBanothu
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
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Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)GiselleginaGloria
3rd International Conference on Artificial Intelligence Advances (AIAD 2024) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the area advanced Artificial Intelligence. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the research area. Core areas of AI and advanced multi-disciplinary and its applications will be covered during the conferences.
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
building confidential and efficient query services in the cloud with rasp data perturbation
1. Building Confidential and Efficient Query Services in the Cloud with RASP Data
Building Confidential and Efficient Query Services in the Cloud
with RASP Data Perturbation
With the wide deployment of public cloud computing infrastructures, using clouds to host data
query services has become an appealing solution for the advantages on scalability and cost-saving.
However, some data might be sensitive that the data owner does not want to move to the
cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a
secured query service should still provide efficient query processing and significantly reduce the
in-house workload to fully realize the benefits of cloud computing. We propose the random
space perturbation (RASP) data perturbation method to provide secure and efficient range query
and kNN query services for protected data in the cloud. The RASP data perturbation method
combines order preserving encryption, dimensionality expansion, random noise injection, and
random projection, to provide strong resilience to attacks on the perturbed data and queries. It
also preserves multidimensional ranges, which allows existing indexing techniques to be applied
to speedup range query processing. The kNN-R algorithm is designed to work with the RASP
range query algorithm to process the kNN queries. We have carefully analyzed the attacks on
data and queries under a precisely defined threat model and realistic security assumptions.
Extensive experiments have been conducted to show the adva ntages of this approach on
efficiency and security.
Requirements for constructing a practical query service in the cloud as the CPEL criteria:
data confidentiality, query privacy, efficient query processing, and low in-house
processing cost. Satisfying these requirements will dramatically increase the complexity
of constructing query services in the cloud. Some related approaches have been
developed to address some aspects of the problem.
The crypto index and order preserving encryption (OPE) are vulnerable to the attacks.
The enhanced crypto index approach puts heavy burden on the in-house infrastructure to
improve the security and privacy.
Contact: 9703109334, 9533694296
ABSTRACT:
EXISTING SYSTEM:
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in
2. Building Confidential and Efficient Query Services in the Cloud with RASP Data
Contact: 9703109334, 9533694296
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in
3. Building Confidential and Efficient Query Services in the Cloud with RASP Data
DISADVANTAGES OF EXISTING SYSTEM:
Do not satisfactorily addressing all aspects of Cloud.
Increase the complexity of constructing query services in the cloud.
Provide slow query services as a result of security and privacy assurance.
We propose the random space perturbation (RASP) data perturbation method to provide
secure and efficient range query and kNN query services for protected data in the cloud.
The RASP data perturbation method combines order preserving encryption,
dimensionality expansion, random noise injection, and random projection, to provide
strong resilience to attacks on the perturbed data and queries.
ADVANTAGES OF PROPOSED SYSTEM:
The RASP perturbation is a unique combination of OPE, dimensionality expansion,
random noise injection, and random projection, which provides strong confidentiality
guarantee.
The RASP approach preserves the topology of multi-dimensional range in secure
transformation, which allows indexing and efficiently query processing.
The proposed service constructions are able to minimize the in-house processing
workload because of the low perturbation cost and high precision query results. This is an
important feature enabling practical cloud-based solutions.
Contact: 9703109334, 9533694296
PROPOSED SYSTEM:
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in
4. Building Confidential and Efficient Query Services in the Cloud with RASP Data
SYSTEM ARCHITECTURE:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System : Pentium IV 2.4 GHz.
Hard Disk : 40 GB.
Floppy Drive : 1.44 Mb.
Monitor : 15 VGA Colour.
Mouse : Logitech.
Ram : 512 Mb.
SOFTWARE REQUIREMENTS:
Operating system : Windows XP/7.
Coding Language : JAVA/J2EE
IDE : Netbeans 7.4
Database : MYSQL
Contact: 9703109334, 9533694296
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in
5. Building Confidential and Efficient Query Services in the Cloud with RASP Data
Huiqi Xu, Shumin Guo, and Keke Chen,“Building Confidential and Efficient Query Services in
the C loud with RASP Data Perturbation”, IEEE TRANSACTIONS ON KNOWLEDGE AND
DATA ENGINEERING, VOL. 26, NO. 2, FEBRUARY 2014.
Contact: 9703109334, 9533694296
REFERENCE:
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in