Google has announced Automatic DLP [Data Loss Prevention], a technology that enables BigQuery customers to identify and classify sensitive data in their data warehouse, as well as define access controls based on those discoveries.
1. Google Cloud Platform Load BalancingIntroductionGoog.docxblondellchancy
1. Google Cloud Platform Load Balancing
Introduction:
Google Cloud Platform Load Balancing enables you to disseminate load-adjusted process assets in single or different locales, to meet your high accessibility prerequisites, to put your assets behind a solitary anycast IP and to scale your assets up or down with keen Autoscaling. Cloud Load Balancing is completely incorporated with Cloud CDN for ideal substance conveyance.
Utilizing Cloud Load Balancing, you can serve content as close as conceivable to your clients, on a framework that can react to more than 1 million questions for each second. Cloud Load Balancing is a completely dispersed, programming characterized, oversaw administration. It isn't occasion or gadget based, so you don't have to deal with a physical burden adjusting foundation.
Types of Cloud Load Balancing:
External load balancing:
Use external load balancing when you need to distribute traffic from the Internet to a GCP network. GCP external load balancing offers the following:
· HTTP or HTTPS traffic: global HTTP(S) Load Balancing
· TCP traffic with SSL offload: global SSL Proxy Load Balancing
· TCP traffic without SSL offload: global TCP Proxy Load Balancing
· UDP traffic: regional Network TCP/UDP Load Balancing
· IPv4 or IPv6 clients
· Global or regional load balancing
Global load balancing requires that you use the Premium Tier.
Internal load balancing:
Use internal load balancing when you need to distribute traffic to instances within a GCP network. GCP Internal TCP/UDP Load Balancing offers the following:
· TCP or UDP traffic
· RFC 1918 load balancing
· Client IP address is preserved
· Health checks
· Autoscaling without prewarming
· Session affinity
· Regional load balancing
GCP Internal HTTP(S) Load Balancing (Beta) offers the following:
· HTTP(S) traffic
· RFC 1918 load balancing
· Health checks
· Autoscaling without prewarming
· Session affinity
· Regional load balancing
2. GCP Security Process:
Introduction:
GCP administrations are intended to convey a more grounded security framework than given by conventional on-premises arrangements. Since Google keeps running on a similar framework made accessible to its clients, associations get similar advantages from these securities.
GCP Security Services:
VPC Service Controls: An instrument that makes and controls a security edge around information put away in API-based administrations like Google Cloud Storage, Big Query, and Bigtable.
Cloud Security Command Center: The device allows clients to view and screen their cloud resources and gives significant security bolster capacities like stockpiling framework filtering, powerlessness identification, and access consents survey.
Access Transparency: Provides clients with a review log of approved regulatory gets to from Google Support and Engineering that tracks action encompassing client information.
Cloud Armor: Cloud Armor is a DDoS and application safeguard administration. It is manufactured .
The document provides an agenda and overview for an introductory Google Cloud Study Jam session. It includes:
1. An agenda that covers an introduction to GCSJ, career benefits, a quiz, Q&A, and next steps.
2. An overview of the Google Cloud Study Jam event which is a 30-day self-paced program covering cloud fundamentals, hands-on labs, and a game prompt engineering project.
3. Details about Google Cloud computing foundations courses that include videos, quizzes, and labs on topics like infrastructure, storage, networking, security, data, machine learning, and AI.
Google Cloud Platform & rockPlace Big Data Event-Mar.31.2016Chris Jang
This document discusses Google Cloud Platform and its data and analytics capabilities. It begins by explaining the evolution of cloud computing models from virtualized data centers to true on-demand cloud services. It then highlights some of Google Cloud Platform's key differentiators like true cloud economics, future-proof infrastructure, access to innovation, and Google-grade security. The document provides overviews of Google Cloud Platform's storage, database, big data, and machine learning offerings and common use cases for each. It also showcases some of Google's innovations in data analytics and machine learning technologies.
GTB Technologies offers a comprehensive data protection and data loss prevention solution called GTB DLP. The solution provides visibility into critical data assets by identifying data, where it is stored, how it is transmitted, who is receiving it, and how it is used. GTB DLP focuses on protecting companies' most critical data and intellectual property assets. It provides unified policy management across network monitoring, endpoint protection, data discovery and classification, and information rights management.
Day Zero on a cloud Project Radu Vunvulea Endava Sofia 2018Radu Vunvulea
In this session we will take a look on what we need to keep on our radar when we start a new project inside cloud. When not only us, but also clients are new in cloud ecosystem we need to be aware of small things that can make a different or secure the success of the project. Things like security, environment isolation and access controls are just a small part of the things that we need be aware. There will be examples from real life projects and hands-on implementations inside Microsoft Azure. After joining this session, you will have an initial view on what things we shall be aware when we start a new project inside cloud.
Critical Breakthroughs and technical Challenges in Big Data Driven Innovation discusses 4 key breakthroughs in Google Cloud Platform's approach to big data:
1. Batch and streaming data processing can be combined using Cloud Dataflow.
2. Real-time data ingestion at massive scales is enabled through technologies like Cloud Bigtable which can process billions of events per hour.
3. Analytics can be done at the speed of thought through BigQuery which allows complex queries on petabytes of data to return results in seconds.
4. Machine learning is made available to everyone through services that offer pre-trained models via APIs and allow custom modeling using TensorFlow on Google Cloud.
IRJET- Security Enhancement for Sharing Data within Group Members in CloudIRJET Journal
1) The document proposes a secure data sharing scheme for sharing data within group members in the cloud. It aims to prevent collusion attacks and allow for secure sharing of data even as group membership changes dynamically.
2) The scheme uses encryption with private keys so that if a user's key is leaked, it will be difficult for others to access the shared data. A trusted authority authenticates users and stores encryption keys to determine responsibility if issues arise.
3) The scheme achieves secure key distribution without requiring secure communication channels by using public key verification. It also allows for fine-grained access control and secure revocation such that revoked users cannot access shared data even by colluding with the cloud.
Buy Google Cloud Accounts Buy Google Cloud Accountskaseyjones247
Now you don’t have to worry about this. We provide high-quality and verified Google Cloud accounts with free credit at a reasonable price. We have a trusted google cloud platform where always google cloud account created for purchase. You can now order from our trusted platform for a Google Cloud account.
1. Google Cloud Platform Load BalancingIntroductionGoog.docxblondellchancy
1. Google Cloud Platform Load Balancing
Introduction:
Google Cloud Platform Load Balancing enables you to disseminate load-adjusted process assets in single or different locales, to meet your high accessibility prerequisites, to put your assets behind a solitary anycast IP and to scale your assets up or down with keen Autoscaling. Cloud Load Balancing is completely incorporated with Cloud CDN for ideal substance conveyance.
Utilizing Cloud Load Balancing, you can serve content as close as conceivable to your clients, on a framework that can react to more than 1 million questions for each second. Cloud Load Balancing is a completely dispersed, programming characterized, oversaw administration. It isn't occasion or gadget based, so you don't have to deal with a physical burden adjusting foundation.
Types of Cloud Load Balancing:
External load balancing:
Use external load balancing when you need to distribute traffic from the Internet to a GCP network. GCP external load balancing offers the following:
· HTTP or HTTPS traffic: global HTTP(S) Load Balancing
· TCP traffic with SSL offload: global SSL Proxy Load Balancing
· TCP traffic without SSL offload: global TCP Proxy Load Balancing
· UDP traffic: regional Network TCP/UDP Load Balancing
· IPv4 or IPv6 clients
· Global or regional load balancing
Global load balancing requires that you use the Premium Tier.
Internal load balancing:
Use internal load balancing when you need to distribute traffic to instances within a GCP network. GCP Internal TCP/UDP Load Balancing offers the following:
· TCP or UDP traffic
· RFC 1918 load balancing
· Client IP address is preserved
· Health checks
· Autoscaling without prewarming
· Session affinity
· Regional load balancing
GCP Internal HTTP(S) Load Balancing (Beta) offers the following:
· HTTP(S) traffic
· RFC 1918 load balancing
· Health checks
· Autoscaling without prewarming
· Session affinity
· Regional load balancing
2. GCP Security Process:
Introduction:
GCP administrations are intended to convey a more grounded security framework than given by conventional on-premises arrangements. Since Google keeps running on a similar framework made accessible to its clients, associations get similar advantages from these securities.
GCP Security Services:
VPC Service Controls: An instrument that makes and controls a security edge around information put away in API-based administrations like Google Cloud Storage, Big Query, and Bigtable.
Cloud Security Command Center: The device allows clients to view and screen their cloud resources and gives significant security bolster capacities like stockpiling framework filtering, powerlessness identification, and access consents survey.
Access Transparency: Provides clients with a review log of approved regulatory gets to from Google Support and Engineering that tracks action encompassing client information.
Cloud Armor: Cloud Armor is a DDoS and application safeguard administration. It is manufactured .
The document provides an agenda and overview for an introductory Google Cloud Study Jam session. It includes:
1. An agenda that covers an introduction to GCSJ, career benefits, a quiz, Q&A, and next steps.
2. An overview of the Google Cloud Study Jam event which is a 30-day self-paced program covering cloud fundamentals, hands-on labs, and a game prompt engineering project.
3. Details about Google Cloud computing foundations courses that include videos, quizzes, and labs on topics like infrastructure, storage, networking, security, data, machine learning, and AI.
Google Cloud Platform & rockPlace Big Data Event-Mar.31.2016Chris Jang
This document discusses Google Cloud Platform and its data and analytics capabilities. It begins by explaining the evolution of cloud computing models from virtualized data centers to true on-demand cloud services. It then highlights some of Google Cloud Platform's key differentiators like true cloud economics, future-proof infrastructure, access to innovation, and Google-grade security. The document provides overviews of Google Cloud Platform's storage, database, big data, and machine learning offerings and common use cases for each. It also showcases some of Google's innovations in data analytics and machine learning technologies.
GTB Technologies offers a comprehensive data protection and data loss prevention solution called GTB DLP. The solution provides visibility into critical data assets by identifying data, where it is stored, how it is transmitted, who is receiving it, and how it is used. GTB DLP focuses on protecting companies' most critical data and intellectual property assets. It provides unified policy management across network monitoring, endpoint protection, data discovery and classification, and information rights management.
Day Zero on a cloud Project Radu Vunvulea Endava Sofia 2018Radu Vunvulea
In this session we will take a look on what we need to keep on our radar when we start a new project inside cloud. When not only us, but also clients are new in cloud ecosystem we need to be aware of small things that can make a different or secure the success of the project. Things like security, environment isolation and access controls are just a small part of the things that we need be aware. There will be examples from real life projects and hands-on implementations inside Microsoft Azure. After joining this session, you will have an initial view on what things we shall be aware when we start a new project inside cloud.
Critical Breakthroughs and technical Challenges in Big Data Driven Innovation discusses 4 key breakthroughs in Google Cloud Platform's approach to big data:
1. Batch and streaming data processing can be combined using Cloud Dataflow.
2. Real-time data ingestion at massive scales is enabled through technologies like Cloud Bigtable which can process billions of events per hour.
3. Analytics can be done at the speed of thought through BigQuery which allows complex queries on petabytes of data to return results in seconds.
4. Machine learning is made available to everyone through services that offer pre-trained models via APIs and allow custom modeling using TensorFlow on Google Cloud.
IRJET- Security Enhancement for Sharing Data within Group Members in CloudIRJET Journal
1) The document proposes a secure data sharing scheme for sharing data within group members in the cloud. It aims to prevent collusion attacks and allow for secure sharing of data even as group membership changes dynamically.
2) The scheme uses encryption with private keys so that if a user's key is leaked, it will be difficult for others to access the shared data. A trusted authority authenticates users and stores encryption keys to determine responsibility if issues arise.
3) The scheme achieves secure key distribution without requiring secure communication channels by using public key verification. It also allows for fine-grained access control and secure revocation such that revoked users cannot access shared data even by colluding with the cloud.
Buy Google Cloud Accounts Buy Google Cloud Accountskaseyjones247
Now you don’t have to worry about this. We provide high-quality and verified Google Cloud accounts with free credit at a reasonable price. We have a trusted google cloud platform where always google cloud account created for purchase. You can now order from our trusted platform for a Google Cloud account.
IRJET- Secure Cloud Data Using Attribute Based EncryptionIRJET Journal
This document proposes a system for secure cloud data storage using attribute-based encryption. It aims to address challenges of key management, defining and enforcing access policies based on data attributes, and enabling keyword search over encrypted data. The system uses multi-authority attribute-based access control (MA-ABAC) to reduce key management complexity for data owners and users. Patient medical records are encrypted and access is determined based on user attributes from professional and personal domains. Attribute-based encryption, proxy re-encryption, and uniquely combining techniques are used to achieve security, key management, user revocation and efficient searches of encrypted data on the cloud.
In this session we will explore how Google's Cloud services (CloudML, Vision, Genomics API) can be used to process genomic and phenotypic data and solve problems in healthcare and agriculture.
Google Cloud and Neo4j: Solving Industry Challenges with Graph Data Analytics...Neo4j
Learn how Google Cloud and Neo4j are addressing the pressing data challenges faced by organizations. In this session, we will delve into the significance of bringing data together and leveraging advanced AI tools to foster innovation. Despite the importance of data insights, a persistent data-to-value gap remains. Discover how the collaboration between Google Cloud and Neo4j bridges this gap and unlocks the true potential of your data. We will explore top use cases in various industry verticals, showcasing how these solutions drive meaningful outcomes. Additionally, gain insights into the seamless integrations Neo4j offers with Google BigQuery and Vertex AI, and learn how our co-solution approach tackles customer problems head-on.
Privacy Preserving in Authentication Protocol for Shared Authority Based Clou...IRJET Journal
This document proposes a privacy-preserving authentication protocol for shared authority-based cloud computing. It discusses security and privacy issues with data sharing among users in cloud storage. The proposed protocol uses a shared authority-based privacy preservation authentication protocol (SecCloud) to address privacy and security concerns for cloud storage. It also uses SecCloud+ to remove data de-duplication. The protocol aims to provide scalability, integrity checking, secure de-duplication, and prevent shoulder surfing attacks during the authentication process in cloud computing.
MongoDB World 2016: Lunch & Learn: Google Cloud for the EnterpriseMongoDB
The document summarizes the evolution of cloud computing and Google Cloud Platform's offerings. It discusses how cloud infrastructure has moved from colocated data centers (1st wave) to virtualized infrastructure (2nd wave) to automated services and scalable data (3rd wave). It then provides an overview of Google Cloud Platform's compute, storage, database, analytics and machine learning services and how they make complex data analysis simpler. The document positions Google Cloud Platform as building on Google's expertise in infrastructure and data to provide customers an advantage.
Privacy preserving computing and secure multi-party computation ISACA AtlantaUlf Mattsson
A major challenge that many organizations faces, is how to address data privacy regulations such as CCPA, GDPR and other emerging regulations around the world, including data residency controls as well as enable data sharing in a secure and private fashion. We will present solutions that can reduce and remove the legal, risk and compliance processes normally associated with data sharing projects by allowing organizations to collaborate across divisions, with other organizations and across jurisdictions where data cannot be relocated or shared.
We will discuss secure multi-party computation where organizations want to securely share sensitive data without revealing their private inputs. We will review solutions that are driving faster time to insight by the use of different techniques for privacy-preserving computing including homomorphic encryption, k-anonymity and differential privacy. We will present best practices and how to control privacy and security throughout the data life cycle. We will also review industry standards, implementations, policy management and case studies for hybrid cloud and on-premises.
A PRACTICAL CLIENT APPLICATION BASED ON ATTRIBUTE-BASED ACCESS CONTROL FOR UN...cscpconf
One of widely used cryptographic primitives for the cloud application is Attribute Based Encryption (ABE) where users can have their own attributes and a ciphertext encrypted by an access policy. Though ABE provides many benefits, the novelty often only exists in an academic world and it is often difficult to find a practical use of ABE for a real application. In this paper, we discuss the design and implementation of a cloud storage client application which supports the concept of ABE. Our proposed client provides an effective access control mechanism where it allows different types of access policy to be defined thus allowing large datasets to be shared by multiple users. Using different access policy, each user only needs to access only a small part of the big data. The goal of our experiment is to explore the right set of strategies for developing a practical ABE-based system. Through the implementation and evaluation, we have determined the various characteristics and issues associated with developing a practical ABEbased
application.
Dell Software: An Opportunity for Channel Partners Dell World
Dell is enhancing its PartnerDirect program to include software solutions and competencies. The program now includes six competency tracks in security, data protection, systems management, and information management. Partners can achieve Premier status through these new competency paths. Cerdant, a Dell partner, has seen its revenues increase 50% by working with Dell and selling Dell's end-to-end solutions. The document encourages partners to engage with Dell sales and marketing, participate in training, and leverage Dell's solutions to help customers turn data into insights, overcome security threats, and accelerate adoption of technologies like virtualization and cloud.
Protecting data privacy in analytics and machine learning ISACA London UKUlf Mattsson
This document discusses privacy-preserving techniques for machine learning and analytics such as homomorphic encryption, secure multi-party computation, differential privacy, and trusted execution environments. It provides examples of how these techniques can be applied, including allowing sensitive financial and healthcare data to be analyzed while preserving privacy. The document also outlines regulatory requirements around data privacy and international standards that techniques must comply with to protect sensitive information.
COST-EFFECTIVE AUTHENTIC AND ANONYMOUS DATA SHARING WITH FORWARD SECURITYShakas Technologies
The document proposes a system for cost-effective, anonymous, and authentic data sharing with forward security. It aims to address issues like efficiency, data integrity, and privacy in large-scale data sharing systems. The system uses identity-based ring signatures to allow anonymous authentication of data by owners. It further enhances security by providing forward security, meaning previously generated signatures remain valid even if a secret key is compromised in the future. The authors provide a concrete scheme, prove its security, and implement it to demonstrate practicality.
User-Centric Privacy Control in Identity Management and Access Control within...IJCI JOURNAL
The ability to effectively implement user-centric privacy controls in cloud-based identity access management (IAM) systems is crucial in today's age of rapidly rising data and increased privacy concerns. The study tackles the scalability issue inside cloud-based IAM systems, where user-centric privacy controls are paramount. The study aims to guarantee effective system performance despite growing numbers of users and data items by following a carefully crafted approach that uses user-centric privacy algorithms. The findings are expected to increase scalability while maintaining security and user privacy, significantly improving current cloud security and IAM techniques. This study provides significant findings for businesses adapting to the changing environment of cloud-based access and identity management, enhancing the security and privacy aspects of the online environment.
Messages addressed to specific users can be decrypted by Key Generation Centre (KGC) by generating their private keys. Data owner wants the data to be delivered only to specified user and not to unauthorized person that is the data owner makes their private data accessible only to authorized person. We propose attribute based encryption and escrow problem which means written agreement delivered to a third party to overcome this problem. Attribute based Encryption (ABE) is a type of public-key encryption in which the private key of a user and the cipher text are dependent upon attributes. It is a promising cryptographic approach.
DECENTRALIZED ACCESS CONTROL OF DATA STORED IN CLOUD USING KEY POLICY ATTRIBU...Migrant Systems
This document proposes a decentralized access control method for data stored in the cloud using key policy attribute-based encryption (KP-ABE). It aims to allow fine-grained access control while maintaining data confidentiality and scaling efficiently. The method defines and implements access policies based on data attributes. It also allows the data owner to delegate access control tasks to cloud servers without revealing data contents. This is achieved using a combination of decentralized KP-ABE and a time-based file deletion scheme. The proposed approach is analyzed and shown to be highly secure and efficient.
This document discusses security challenges related to big data and Hadoop. It notes that as data grows exponentially, the complexity of managing, securing, and enforcing privacy restrictions on data sets increases. Organizations now need to control access to data scientists based on authorization levels and what data they are allowed to see. Mismanagement of data sets can be costly, as shown by incidents at AOL, Netflix, and a Massachusetts hospital that led to lawsuits and fines. The document then provides a brief history of Hadoop security, noting that it was originally developed without security in mind. It outlines the current Kerberos-centric security model and talks about some vendor solutions emerging to enhance Hadoop security. Finally, it provides guidance on developing security and privacy
Secure Data Sharing In an Untrusted CloudIJERA Editor
Cloud computing is a huge area which basically provides many services on the basis of pay as you go. One of the fundamental services provided by cloud is data storage. Cloud provides cost efficiency and an efficient solution for sharing resource among cloud users. A secure and efficient data sharing scheme for groups in cloud is not an easy task. On one hand customers are not ready to share their identity but on other hand want to enjoy the cost efficiency provided by the cloud. It needs to provide identity privacy, multiple owner and dynamic data sharing without getting effected by the number of cloud users revoked. In this paper, any member of a group can completely enjoy the data storing and sharing services by the cloud. A secure data sharing scheme for dynamic cloud users is proposed in this paper. For which it uses group signature and dynamic broadcast encryption techniques such that any user in a group can share the information in a secured manner. Additionally the permission option is proposed for the security reasons. This means the file access permissions are generated by the admin and given to the user using Role Based Access Control (RBA) algorithm. The file access permissions are read, write and delete. In this, owner can provide files with options and accepts the users using that option. The revocation of cloud user is a function generated by the Admin for security purpose. The encryption computational cost and storage overhead is not dependent on the number of users revoked. We analyze the security by proofs and produce the cloud efficiency report using cloudsim.
A Noval Method for Data Auditing and Integrity Checking in Public Cloudrahulmonikasharma
Data plays a huge role in today’s era. All business requires to deal with lot of business. So data has to be secured correctly. In this paper we aim to design a system to help to protect the data in the cloud. The public cloud is used in which the users stores the data and the data is secured by using the cryptographic method. Every customer wants to store the data and access or process the data from the cloud, but the major setback is security issues. In this paper we present a novel algorithm which helps the data to be accessed securely from the cloud.
This document discusses security risks associated with cloud computing and databases. The main security risks are data breaches, data loss, and service hijacking that can occur when sensitive data is stored in cloud databases. Two examples of past data breaches at large companies, Home Depot and Target, are described along with the steps they took to strengthen security and regain customer trust. Methods to overcome security challenges in cloud computing discussed are encrypting data, implementing strong key management practices, and giving users control over their encryption keys.
KEY AGGREGATE CRYPTOSYSTEM FOR SCALABLE DATA SHARING IN CLOUDNaseem nisar
1. EASiER proposes an encryption-based access control architecture for social networks that uses attribute-based encryption. It introduces a minimally trusted proxy to enable efficient revocation without reissuing keys.
2. Multi-authority attribute based encryption schemes allow multiple authorities to issue secret keys for attributes. This is useful in applications with attributes managed by different authorities.
3. Existing social network privacy architectures focus on encryption-based access control but do not address efficient revocation of users or attributes. EASiER addresses this issue.
Five Cutting Edge Uses for Analytics in the EnterpriseInfoTrust LLC
Presentation by Jesse Nichols from Google. Five cutting edge uses for analytics in the enterprise. Presentation covers how to capture and measure consumer moments that matter in one place.
Five Essential Techniques to Prevent Data Leaks - ITSecurityWire.pdfEnterprise Insider
Data leakage of sensitive proprietary or consumer data can cause a downward spiral for firms. Despite how hard everyone tries to keep their data safe and secure, it appears that all it takes is one clever hack or one safety precaution to be ineffective for sensitive information to fall into vicious hands.
Read in detail about the techniques: https://bit.ly/3J6oK53
Dark Data Management_ Mitigating the Risks of the Invisible - EnterpriseTalk.pdfEnterprise Insider
Dark data is a major challenge in enterprises, and it’s not going away soon. Fortunately, there are ways to reduce dark data and the risks that come with it.
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This document proposes a system for secure cloud data storage using attribute-based encryption. It aims to address challenges of key management, defining and enforcing access policies based on data attributes, and enabling keyword search over encrypted data. The system uses multi-authority attribute-based access control (MA-ABAC) to reduce key management complexity for data owners and users. Patient medical records are encrypted and access is determined based on user attributes from professional and personal domains. Attribute-based encryption, proxy re-encryption, and uniquely combining techniques are used to achieve security, key management, user revocation and efficient searches of encrypted data on the cloud.
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This document proposes a privacy-preserving authentication protocol for shared authority-based cloud computing. It discusses security and privacy issues with data sharing among users in cloud storage. The proposed protocol uses a shared authority-based privacy preservation authentication protocol (SecCloud) to address privacy and security concerns for cloud storage. It also uses SecCloud+ to remove data de-duplication. The protocol aims to provide scalability, integrity checking, secure de-duplication, and prevent shoulder surfing attacks during the authentication process in cloud computing.
MongoDB World 2016: Lunch & Learn: Google Cloud for the EnterpriseMongoDB
The document summarizes the evolution of cloud computing and Google Cloud Platform's offerings. It discusses how cloud infrastructure has moved from colocated data centers (1st wave) to virtualized infrastructure (2nd wave) to automated services and scalable data (3rd wave). It then provides an overview of Google Cloud Platform's compute, storage, database, analytics and machine learning services and how they make complex data analysis simpler. The document positions Google Cloud Platform as building on Google's expertise in infrastructure and data to provide customers an advantage.
Privacy preserving computing and secure multi-party computation ISACA AtlantaUlf Mattsson
A major challenge that many organizations faces, is how to address data privacy regulations such as CCPA, GDPR and other emerging regulations around the world, including data residency controls as well as enable data sharing in a secure and private fashion. We will present solutions that can reduce and remove the legal, risk and compliance processes normally associated with data sharing projects by allowing organizations to collaborate across divisions, with other organizations and across jurisdictions where data cannot be relocated or shared.
We will discuss secure multi-party computation where organizations want to securely share sensitive data without revealing their private inputs. We will review solutions that are driving faster time to insight by the use of different techniques for privacy-preserving computing including homomorphic encryption, k-anonymity and differential privacy. We will present best practices and how to control privacy and security throughout the data life cycle. We will also review industry standards, implementations, policy management and case studies for hybrid cloud and on-premises.
A PRACTICAL CLIENT APPLICATION BASED ON ATTRIBUTE-BASED ACCESS CONTROL FOR UN...cscpconf
One of widely used cryptographic primitives for the cloud application is Attribute Based Encryption (ABE) where users can have their own attributes and a ciphertext encrypted by an access policy. Though ABE provides many benefits, the novelty often only exists in an academic world and it is often difficult to find a practical use of ABE for a real application. In this paper, we discuss the design and implementation of a cloud storage client application which supports the concept of ABE. Our proposed client provides an effective access control mechanism where it allows different types of access policy to be defined thus allowing large datasets to be shared by multiple users. Using different access policy, each user only needs to access only a small part of the big data. The goal of our experiment is to explore the right set of strategies for developing a practical ABE-based system. Through the implementation and evaluation, we have determined the various characteristics and issues associated with developing a practical ABEbased
application.
Dell Software: An Opportunity for Channel Partners Dell World
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This document discusses privacy-preserving techniques for machine learning and analytics such as homomorphic encryption, secure multi-party computation, differential privacy, and trusted execution environments. It provides examples of how these techniques can be applied, including allowing sensitive financial and healthcare data to be analyzed while preserving privacy. The document also outlines regulatory requirements around data privacy and international standards that techniques must comply with to protect sensitive information.
COST-EFFECTIVE AUTHENTIC AND ANONYMOUS DATA SHARING WITH FORWARD SECURITYShakas Technologies
The document proposes a system for cost-effective, anonymous, and authentic data sharing with forward security. It aims to address issues like efficiency, data integrity, and privacy in large-scale data sharing systems. The system uses identity-based ring signatures to allow anonymous authentication of data by owners. It further enhances security by providing forward security, meaning previously generated signatures remain valid even if a secret key is compromised in the future. The authors provide a concrete scheme, prove its security, and implement it to demonstrate practicality.
User-Centric Privacy Control in Identity Management and Access Control within...IJCI JOURNAL
The ability to effectively implement user-centric privacy controls in cloud-based identity access management (IAM) systems is crucial in today's age of rapidly rising data and increased privacy concerns. The study tackles the scalability issue inside cloud-based IAM systems, where user-centric privacy controls are paramount. The study aims to guarantee effective system performance despite growing numbers of users and data items by following a carefully crafted approach that uses user-centric privacy algorithms. The findings are expected to increase scalability while maintaining security and user privacy, significantly improving current cloud security and IAM techniques. This study provides significant findings for businesses adapting to the changing environment of cloud-based access and identity management, enhancing the security and privacy aspects of the online environment.
Messages addressed to specific users can be decrypted by Key Generation Centre (KGC) by generating their private keys. Data owner wants the data to be delivered only to specified user and not to unauthorized person that is the data owner makes their private data accessible only to authorized person. We propose attribute based encryption and escrow problem which means written agreement delivered to a third party to overcome this problem. Attribute based Encryption (ABE) is a type of public-key encryption in which the private key of a user and the cipher text are dependent upon attributes. It is a promising cryptographic approach.
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This document proposes a decentralized access control method for data stored in the cloud using key policy attribute-based encryption (KP-ABE). It aims to allow fine-grained access control while maintaining data confidentiality and scaling efficiently. The method defines and implements access policies based on data attributes. It also allows the data owner to delegate access control tasks to cloud servers without revealing data contents. This is achieved using a combination of decentralized KP-ABE and a time-based file deletion scheme. The proposed approach is analyzed and shown to be highly secure and efficient.
This document discusses security challenges related to big data and Hadoop. It notes that as data grows exponentially, the complexity of managing, securing, and enforcing privacy restrictions on data sets increases. Organizations now need to control access to data scientists based on authorization levels and what data they are allowed to see. Mismanagement of data sets can be costly, as shown by incidents at AOL, Netflix, and a Massachusetts hospital that led to lawsuits and fines. The document then provides a brief history of Hadoop security, noting that it was originally developed without security in mind. It outlines the current Kerberos-centric security model and talks about some vendor solutions emerging to enhance Hadoop security. Finally, it provides guidance on developing security and privacy
Secure Data Sharing In an Untrusted CloudIJERA Editor
Cloud computing is a huge area which basically provides many services on the basis of pay as you go. One of the fundamental services provided by cloud is data storage. Cloud provides cost efficiency and an efficient solution for sharing resource among cloud users. A secure and efficient data sharing scheme for groups in cloud is not an easy task. On one hand customers are not ready to share their identity but on other hand want to enjoy the cost efficiency provided by the cloud. It needs to provide identity privacy, multiple owner and dynamic data sharing without getting effected by the number of cloud users revoked. In this paper, any member of a group can completely enjoy the data storing and sharing services by the cloud. A secure data sharing scheme for dynamic cloud users is proposed in this paper. For which it uses group signature and dynamic broadcast encryption techniques such that any user in a group can share the information in a secured manner. Additionally the permission option is proposed for the security reasons. This means the file access permissions are generated by the admin and given to the user using Role Based Access Control (RBA) algorithm. The file access permissions are read, write and delete. In this, owner can provide files with options and accepts the users using that option. The revocation of cloud user is a function generated by the Admin for security purpose. The encryption computational cost and storage overhead is not dependent on the number of users revoked. We analyze the security by proofs and produce the cloud efficiency report using cloudsim.
A Noval Method for Data Auditing and Integrity Checking in Public Cloudrahulmonikasharma
Data plays a huge role in today’s era. All business requires to deal with lot of business. So data has to be secured correctly. In this paper we aim to design a system to help to protect the data in the cloud. The public cloud is used in which the users stores the data and the data is secured by using the cryptographic method. Every customer wants to store the data and access or process the data from the cloud, but the major setback is security issues. In this paper we present a novel algorithm which helps the data to be accessed securely from the cloud.
This document discusses security risks associated with cloud computing and databases. The main security risks are data breaches, data loss, and service hijacking that can occur when sensitive data is stored in cloud databases. Two examples of past data breaches at large companies, Home Depot and Target, are described along with the steps they took to strengthen security and regain customer trust. Methods to overcome security challenges in cloud computing discussed are encrypting data, implementing strong key management practices, and giving users control over their encryption keys.
KEY AGGREGATE CRYPTOSYSTEM FOR SCALABLE DATA SHARING IN CLOUDNaseem nisar
1. EASiER proposes an encryption-based access control architecture for social networks that uses attribute-based encryption. It introduces a minimally trusted proxy to enable efficient revocation without reissuing keys.
2. Multi-authority attribute based encryption schemes allow multiple authorities to issue secret keys for attributes. This is useful in applications with attributes managed by different authorities.
3. Existing social network privacy architectures focus on encryption-based access control but do not address efficient revocation of users or attributes. EASiER addresses this issue.
Five Cutting Edge Uses for Analytics in the EnterpriseInfoTrust LLC
Presentation by Jesse Nichols from Google. Five cutting edge uses for analytics in the enterprise. Presentation covers how to capture and measure consumer moments that matter in one place.
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Despite recent breaches raising awareness, Cloud supply chain threats are not going away. In fact, because of the COVID-19 pandemic, cloud usage has accelerated, and the threats may have increased. So, what’s the source of the problem? The main sources of risk in the cloud supply chain include siloed processes, ecosystem complexity, and a lack of visibility into software assets, all of which result from poor risk management.
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Many businesses have developed and implemented a variety of AI use cases. However, to become a truly AI-enabled organization, several standalone use cases must be developed, maintained, and deployed to address various challenges across the enterprise. Machine Learning Operations (MLOps) promises to make it seamless to leverage the potential of AI without hassle.
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In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
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The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
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UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
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2. Understanding how this integration enhances test automation within the UiPath platform
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What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
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Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
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My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Google Cloud Unveils Its New Data Loss Prevention Tool for Bigquery.pdf
1. 4/18/22, 7:35 PM Google Cloud Unveils Its New Data Loss Prevention Tool for Bigquery
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Google Cloud Unveils Its New Data Loss Prevention
Tool for Bigquery
Google has announced Automatic DLP [Data Loss Prevention], a technology that enables BigQuery
customers to identify and classify sensitive data in their data warehouse, as well as define access
controls based on those discoveries.
Automatic DLP was previously accessible in public preview and is now available in its entirety.
Google is also providing customers with additional options to control the frequency and parameters
under which their data is profiled.
According to Google, the Cloud DLP platform also includes capabilities for conducting on-demand
thorough and extensive data inspections and assisting customers in obscuring, masking, or
tokenizing data in order to decrease overall data risk.
Read More: https://techcrunch.com/2022/04/14/google-cloud-launches-its-new-data-loss-
prevention-tool-for-bigquery/
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News Enterprisetalk News.
ET Bureau
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By ET Bureau - April 15, 2022
2. 4/18/22, 7:35 PM Google Cloud Unveils Its New Data Loss Prevention Tool for Bigquery
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