The multimedia big data in multimedia sensing and other IoT (Internet of Things) systems are high-volume, real-time, dynamic and heterogeneous. These characteristics lead to new challenges of data security. When computation and power resources in some IoT nodes are very scarce, these challenges become more serious that complex data security process on multimedia data is restricted by the aforementioned limited resources. Hence, the confidentiality of multimedia big data under resources constraints is investigated in this paper. Firstly, the growth trend of data volume compared with computational resources is discussed, and an analysis model for multimedia data encryption optimization is proposed. Secondly, a general-purpose lightweight speed tunable video encryption scheme is introduced. Thirdly, a series of intelligent selective encryption control models are proposed. Fourthly, the performance of proposed schemes is evaluated by experimental analyses and proves that schemes are effective enough to support real-time encryption of multimedia big data. Additionally, in the age of big data and cloud computing, the aforementioned analysis method can also be applied to other systems with limited resources.
{"title":"A Multi-level Intelligent Selective Encryption Control Model for Multimedia Big Data Security in Sensing System with Resource Constraints","authors":"Chen Xiao, Lifeng Wang, Zhu Jie, Tiemeng Chen","doi":"10.1109/CSCloud.2016.37","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.37","url":null,"abstract":"The multimedia big data in multimedia sensing and other IoT (Internet of Things) systems are high-volume, real-time, dynamic and heterogeneous. These characteristics lead to new challenges of data security. When computation and power resources in some IoT nodes are very scarce, these challenges become more serious that complex data security process on multimedia data is restricted by the aforementioned limited resources. Hence, the confidentiality of multimedia big data under resources constraints is investigated in this paper. Firstly, the growth trend of data volume compared with computational resources is discussed, and an analysis model for multimedia data encryption optimization is proposed. Secondly, a general-purpose lightweight speed tunable video encryption scheme is introduced. Thirdly, a series of intelligent selective encryption control models are proposed. Fourthly, the performance of proposed schemes is evaluated by experimental analyses and proves that schemes are effective enough to support real-time encryption of multimedia big data. Additionally, in the age of big data and cloud computing, the aforementioned analysis method can also be applied to other systems with limited resources.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127070000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Consolidation is an approach that optimizes the utilization of the computing resources. Many datacenter virtualization solutions employ it to reduce the number of physical machines (PMs), and thus save money on hardware, cooling, and electricity. Resources oversubscription, as an effective resource management policy is used to free idle resource in cloud. More virtual machines (VMs) can host in the same PM. However, the limited underlying resource (such as last level cache or I/O bandwidth) provided by PM cannot support much more VMs. The performance of VMs may not be guaranteed due to the underlying resources starvation. Meanwhile, the oversubscription strategy is failure for some VMs when they are migrated to a new PM according to consolidation strategy. In this paper, we present a performance-aware consolidation strategy targeting the oversubscribed cloud. It constructs a performance alarm and the corresponding threshold to control the density of consolidation and maintain the performance isolation among the co-hosting VMs. It also finds the appropriate combination of VMs hosting in the same PM to better support oversubscription. We performed our evaluation on a virtual datacenter simulated by Xen. Our evaluation results show that performance alarm effectively stop the performance isolation destroyed. Furthermore, the policy of VM placement reduces the number of used PMs and also protects maximum oversubscription for each VM.
{"title":"A Consolidation Strategy Supporting Resources Oversubscription in Cloud Computing","authors":"Y. Liu","doi":"10.1109/CSCloud.2016.21","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.21","url":null,"abstract":"Consolidation is an approach that optimizes the utilization of the computing resources. Many datacenter virtualization solutions employ it to reduce the number of physical machines (PMs), and thus save money on hardware, cooling, and electricity. Resources oversubscription, as an effective resource management policy is used to free idle resource in cloud. More virtual machines (VMs) can host in the same PM. However, the limited underlying resource (such as last level cache or I/O bandwidth) provided by PM cannot support much more VMs. The performance of VMs may not be guaranteed due to the underlying resources starvation. Meanwhile, the oversubscription strategy is failure for some VMs when they are migrated to a new PM according to consolidation strategy. In this paper, we present a performance-aware consolidation strategy targeting the oversubscribed cloud. It constructs a performance alarm and the corresponding threshold to control the density of consolidation and maintain the performance isolation among the co-hosting VMs. It also finds the appropriate combination of VMs hosting in the same PM to better support oversubscription. We performed our evaluation on a virtual datacenter simulated by Xen. Our evaluation results show that performance alarm effectively stop the performance isolation destroyed. Furthermore, the policy of VM placement reduces the number of used PMs and also protects maximum oversubscription for each VM.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126991813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Privacy issues have become a considerable issue while the applications of big data are growing dramatically fast in cloud computing. The benefits us implementing these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The time execution of encrypting data is one of the serious issues during the processes of data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level, companions with privacy concerns. In this paper, we concentrate on privacy issue and propose a novel data encryption approach, named as Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data using privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements. The performance of D2ES has been evaluated in our experiments, which provides the proof of the privacy enhancement.
{"title":"Privacy-Aware Adaptive Data Encryption Strategy of Big Data in Cloud Computing","authors":"Keke Gai, Meikang Qiu, Hui Zhao, Jian Xiong","doi":"10.1109/CSCloud.2016.52","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.52","url":null,"abstract":"Privacy issues have become a considerable issue while the applications of big data are growing dramatically fast in cloud computing. The benefits us implementing these emerging technologies have improved or changed service models and improve application performances in various perspectives. However, the remarkably growing volume of data sizes has also resulted in many challenges in practice. The time execution of encrypting data is one of the serious issues during the processes of data processing and transmissions. Many current applications abandon data encryptions in order to reach an adoptive performance level, companions with privacy concerns. In this paper, we concentrate on privacy issue and propose a novel data encryption approach, named as Dynamic Data Encryption Strategy (D2ES). Our proposed approach aims to selectively encrypt data using privacy classification methods under timing constraints. This approach is designed to maximize the privacy protection scope by using a selective encryption strategy within the required execution time requirements. The performance of D2ES has been evaluated in our experiments, which provides the proof of the privacy enhancement.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127031959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we propose a sensor-cloud environment which incorporates remotely located physical sensor devices in the generic resource family along with other resources like CPU, memory etc. The environment enables ubiquitous and on-demand access to virtual sensors which are abstractions of physical devices with enhanced capabilities. An architecture of the sensor-cloud environment is discussed in this paper that spans multiple infrastructures hosting local and remote resources. Responsibilities of each of the components in the sensor-cloud architecture are defined and algorithms for provisioning these components are discussed along with some proposed enhancements with respect to our earlier work. As forwarding data traffic is an important task, a high performance I/O redirection algorithm is also proposed to deliver sensed traffic to multiple instances of virtual sensors that is accumulated from one or multiple physical sensors. Performance of the I/O redirection algorithm is observed and the experimental results are presented in this paper.
{"title":"SensIaas: A Sensor-Cloud Infrastructure with Sensor Virtualization","authors":"Sunanda Bose, N. Mukherjee","doi":"10.1109/CSCloud.2016.28","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.28","url":null,"abstract":"In this paper, we propose a sensor-cloud environment which incorporates remotely located physical sensor devices in the generic resource family along with other resources like CPU, memory etc. The environment enables ubiquitous and on-demand access to virtual sensors which are abstractions of physical devices with enhanced capabilities. An architecture of the sensor-cloud environment is discussed in this paper that spans multiple infrastructures hosting local and remote resources. Responsibilities of each of the components in the sensor-cloud architecture are defined and algorithms for provisioning these components are discussed along with some proposed enhancements with respect to our earlier work. As forwarding data traffic is an important task, a high performance I/O redirection algorithm is also proposed to deliver sensed traffic to multiple instances of virtual sensors that is accumulated from one or multiple physical sensors. Performance of the I/O redirection algorithm is observed and the experimental results are presented in this paper.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"2009 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129620034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As a recent emerging industry, cybersecurity insurance has been growing ambitiously fast, which mainly serves the financial industry and assists financial firms to reduce cybersecurity risks. Understanding the risk classification is an important hemisphere for operating cybersecurity insurance. However, the classification representation will be complicated when the service system becomes large. Improper presentation of the risks can result in financial loss or operational mistakes. This paper addresses this concern and proposes an approach using ontology-based knowledge representation for cybersecurity insurance. The approach is named as Semantic Cyber Incident Classification (SCIC) model, which uses knowledge representation deriving from semantic techniques. Our approach is specifically designed for targeting at cybersecurity insurance domain, which has been assessed by our experiments.
{"title":"Cyber Incident Classifications Using Ontology-Based Knowledge Representation for Cybersecurity Insurance in Financial Industry","authors":"S. Elnagdy, Meikang Qiu, Keke Gai","doi":"10.1109/CSCloud.2016.45","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.45","url":null,"abstract":"As a recent emerging industry, cybersecurity insurance has been growing ambitiously fast, which mainly serves the financial industry and assists financial firms to reduce cybersecurity risks. Understanding the risk classification is an important hemisphere for operating cybersecurity insurance. However, the classification representation will be complicated when the service system becomes large. Improper presentation of the risks can result in financial loss or operational mistakes. This paper addresses this concern and proposes an approach using ontology-based knowledge representation for cybersecurity insurance. The approach is named as Semantic Cyber Incident Classification (SCIC) model, which uses knowledge representation deriving from semantic techniques. Our approach is specifically designed for targeting at cybersecurity insurance domain, which has been assessed by our experiments.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128126489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The dramatical development of Web-based technology has been empowering enormous change in various domains. Cloud-based solutions have remarkably widened business models with multiple value creation channels. The financial industry is a major beneficiary of leveraging these emerging new technologies, such as big data and cloud-related services. This great changing trend has also led to a great concern in cybersecurity. Under this background, cybersecurity insurance is a growing domain in the financial industry. However, cybersecurity insurance industry also encounters a variety of cyber concerns while the Web-based approaches are applied. This paper focuses on this issue and review a broad scope of materials to gain a deep understanding of taxonomy of cyber security risks for cybersecurity insurance. The findings of this work can guide the cybersecurity insurance practitioners to avoid as much risk as possible as well as create potential solutions to the possible risks.
{"title":"Understanding Taxonomy of Cyber Risks for Cybersecurity Insurance of Financial Industry in Cloud Computing","authors":"S. Elnagdy, Meikang Qiu, Keke Gai","doi":"10.1109/CSCloud.2016.46","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.46","url":null,"abstract":"The dramatical development of Web-based technology has been empowering enormous change in various domains. Cloud-based solutions have remarkably widened business models with multiple value creation channels. The financial industry is a major beneficiary of leveraging these emerging new technologies, such as big data and cloud-related services. This great changing trend has also led to a great concern in cybersecurity. Under this background, cybersecurity insurance is a growing domain in the financial industry. However, cybersecurity insurance industry also encounters a variety of cyber concerns while the Web-based approaches are applied. This paper focuses on this issue and review a broad scope of materials to gain a deep understanding of taxonomy of cyber security risks for cybersecurity insurance. The findings of this work can guide the cybersecurity insurance practitioners to avoid as much risk as possible as well as create potential solutions to the possible risks.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132590712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditionally resource utilization on physical servers in cloud data center is uncertain. On one hand, resources will be wasted if the assignment of tasks are not enough. On the other hand it will cause overload if the assignment of tasks are too much. This is especially obvious when the applications are the same type. To solve this issue and considering CPU intensive application is one of the most common type of application in cloud, we have studied the optimization strategy for this kind of applications on the same server. According to resource preferences of different types of applications, we analyze the case that multiple CPU intensive applications run simultaneously, and put forward a model which can make a prediction of execution time for this case. Extensive experiments show that the model is suitable for CPU intensive applications, and it can accurately predict their execution time. In order to improve the execution efficiency of applications, we propose a scheduling model for CPU intensive applications. Experiments show that the scheduling model can improve the execution efficiency of applications effectively and optimize the resource utilization.
{"title":"Resource Optimization Strategy for CPU Intensive Applications in Cloud Computing Environment","authors":"Jun-jie Peng, Jinbao Chen, Shuai Kong, Danxu Liu, Meikang Qiu","doi":"10.1109/CSCloud.2016.29","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.29","url":null,"abstract":"Traditionally resource utilization on physical servers in cloud data center is uncertain. On one hand, resources will be wasted if the assignment of tasks are not enough. On the other hand it will cause overload if the assignment of tasks are too much. This is especially obvious when the applications are the same type. To solve this issue and considering CPU intensive application is one of the most common type of application in cloud, we have studied the optimization strategy for this kind of applications on the same server. According to resource preferences of different types of applications, we analyze the case that multiple CPU intensive applications run simultaneously, and put forward a model which can make a prediction of execution time for this case. Extensive experiments show that the model is suitable for CPU intensive applications, and it can accurately predict their execution time. In order to improve the execution efficiency of applications, we propose a scheduling model for CPU intensive applications. Experiments show that the scheduling model can improve the execution efficiency of applications effectively and optimize the resource utilization.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130793526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A novel paradigm to detect smartphone physical capture attacks is proposed. Using received signal strength indicator and general system problem solving framework, the paradigm recognizes indoors moving pattern of a phone user. Most existing approaches in detection of physical capture attacks focus on protecting the network not the device. This paradigm concentrates primarily on safeguarding the security and privacy of individual users. An extra security layer, which is similar to the protection offered by biometrics techniques, is added. With this augmented defense, the user can considerably enhance both confidentiality and integrity of her information on the mobile device. At minimum the permanent deletion should thwart illegal access. More effective protections can also be implemented as similar ease. Furthermore, easy to use algorithms have been created to simplify and streamline the pattern generation and selection procedures of traditional general system problem solving framework. Experiments on Android smartphone have proved effectiveness and efficiency of this paradigm.
{"title":"A Universal Algorithm to Secure Stolen Mobile Devices Using Wi-Fi in Indoors Environments","authors":"Wei Ding, Jose Arriaga","doi":"10.1109/CSCloud.2016.54","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.54","url":null,"abstract":"A novel paradigm to detect smartphone physical capture attacks is proposed. Using received signal strength indicator and general system problem solving framework, the paradigm recognizes indoors moving pattern of a phone user. Most existing approaches in detection of physical capture attacks focus on protecting the network not the device. This paradigm concentrates primarily on safeguarding the security and privacy of individual users. An extra security layer, which is similar to the protection offered by biometrics techniques, is added. With this augmented defense, the user can considerably enhance both confidentiality and integrity of her information on the mobile device. At minimum the permanent deletion should thwart illegal access. More effective protections can also be implemented as similar ease. Furthermore, easy to use algorithms have been created to simplify and streamline the pattern generation and selection procedures of traditional general system problem solving framework. Experiments on Android smartphone have proved effectiveness and efficiency of this paradigm.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116147820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Time synchronization is very important in the IEEE802.15.4e network which aim to industrial automation applications. It enabled high end-to-end reliability and low power wireless networking. If an adversary launches time synchronization attacks to the IEEE802.15.4e networks, the whole network communications will be paralyzed. In this paper, we present two types of attacks: 1) ASN and 2) time synchronization tree attack. In ASN attack the legitimate nodes may get an incorrect ASN value and thus can't synchronize to the normal network, while in time synchronization tree attack, the attacker can damage the structure of time synchronization tree by faking DIO packets. We propose some countermeasures which include intrusion detection algorithms, Encryption and Authentication methods to defend against these attacks. Finally, we perform time synchronization tree attack experiments. The experiment results show that the proposed mechanisms can defend against the attack.
{"title":"Security Vulnerabilities and Countermeasures for Time Synchronization in IEEE802.15.4e Networks","authors":"Wei Yang, Qin Wang, Yadong Wan, Jie He","doi":"10.1109/CSCloud.2016.44","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.44","url":null,"abstract":"Time synchronization is very important in the IEEE802.15.4e network which aim to industrial automation applications. It enabled high end-to-end reliability and low power wireless networking. If an adversary launches time synchronization attacks to the IEEE802.15.4e networks, the whole network communications will be paralyzed. In this paper, we present two types of attacks: 1) ASN and 2) time synchronization tree attack. In ASN attack the legitimate nodes may get an incorrect ASN value and thus can't synchronize to the normal network, while in time synchronization tree attack, the attacker can damage the structure of time synchronization tree by faking DIO packets. We propose some countermeasures which include intrusion detection algorithms, Encryption and Authentication methods to defend against these attacks. Finally, we perform time synchronization tree attack experiments. The experiment results show that the proposed mechanisms can defend against the attack.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128682198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years cloud computing is getting more and more attention every day. While outsourcing the hardware and software resources, still being able to manage them remotely with benefits like high computing power, competitiveness, cost efficiency, scalability, flexibility, accessibility and availability are revolutionary. For all of its advantages, on the other hand, nothing interesting is ever completely one-sided. Security and integrity of the data which is stored in untrustworthy server is critically important and raises concerns about it. The data can be modified, removed, corrupted or even stolen since it is in the remote server. These kinds of malicious activities can be done either by untrusted server or unauthorized user(s). Therefore, various integrity checking methods have been offered for cloud computing systems. This survey aims to analyze and compare different researches about data integrity proofs for these systems.
{"title":"Survey on Data Integrity in Cloud","authors":"K. N. Sevis, Ensar Seker","doi":"10.1109/CSCloud.2016.35","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.35","url":null,"abstract":"In recent years cloud computing is getting more and more attention every day. While outsourcing the hardware and software resources, still being able to manage them remotely with benefits like high computing power, competitiveness, cost efficiency, scalability, flexibility, accessibility and availability are revolutionary. For all of its advantages, on the other hand, nothing interesting is ever completely one-sided. Security and integrity of the data which is stored in untrustworthy server is critically important and raises concerns about it. The data can be modified, removed, corrupted or even stolen since it is in the remote server. These kinds of malicious activities can be done either by untrusted server or unauthorized user(s). Therefore, various integrity checking methods have been offered for cloud computing systems. This survey aims to analyze and compare different researches about data integrity proofs for these systems.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133977027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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