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【数据安全】PSDS–Proficient Security Over Distributed Storage: A Method for Data Transmission in Cloud*
Abstract：
Cloud Computing facilitates business by storing an enormous amount of data in the cloud transmitted over the Internet with seamless access to the data and no hardware compatibility limitations.However, data during transmission is vulnerable to man in middle,known plain text, chosen cipher text, related key and pollution attack.Therefore,uploading data on a single cloud may increase the risk of damage to the confidential data. Existing literature study uncovered multiple cryptography techniques such as SA-EDS, Reliable Framework for Data Administration (RFDA), Encryption and Splitting Technique (EST) to secure data storage over multi-cloud.However,existing methods are vulnerable to numerous attacks. This article emphasis on data security issues over multi cloud and proposes a Proficient Security over Distributed Storage (PSDS) method.PSDS divides the data is into two categories; normal and sensitive, further more the sensitive data is further divided into two parts. Each part is encrypted and distributed over multi-cloud whereas the normal data is uploaded on a single cloud in encrypted form. At the decryption stage, sensitive data is merged from multi-cloud. The PSDS is tested against multiple attacks and it has been concluded that it is resistant to related key attack, pollution attack, chosen ciphertext attack, and known plain text attack. Furthermore, PSDS has less computational time as compared to the STTN and RFD encryption method.
摘要：云计算通过在云端存储大量数据通过促进了商业发展，它通过网络无缝存储数据，并且没有硬件兼容限制。然而，数据在传输过程中，中间人攻击是非常脆弱的，已知纯文本，选择密文、相关秘钥和污染攻击。因此，在单个云平台上上传数据，会增加机密数据毁灭的风险。现存的文学研究解密了多种密码学技术，例如SA-EDs，数据管理可信赖框架（RFDA），加密和切分技术来确保在多重云上的数据存储。然而，现存的方法对于许多攻击是脆弱的。本文强调在多重云上的数据安全问题，并提出了PSDS方法。PSDS将数据分成了两类，正常的和敏感的，敏感数据被进一步划分成两部分。每部分都是加密的，并且分布在多云端，一般的数据都是以加密的形式在单个云平台上传。在解密阶段，敏感数据从多个平台融合，PSDS测试对抗多种攻击，并且总结得出它抵制相关的密钥攻击，污染攻击，被选密文攻击，和已知纯文本攻击。而且，PSDS比起STTN和RFD加密方法，计算时间更少。
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【边缘计算数据的K-V存储】Distributed Key-Value Storage for Edge Computing and its Explicit Data Distribution Method
Abstract:
In this article, we provide a data framework for edge computing where developers can easily attain efficient data transfer between devices or users. We propose a distributed key-value storage platform for edge computing and its explicit data distribution management method. In this platform, edge servers organize the distributed key-value storage in a uniform namespace. To enable fast data access to a record in edge computing, the allocation strategy of the record and its cache on the edge servers is important. Our platform offers a distributed objects that can dynamically change home server and allocate cache objects following user defined rules. A rule is defined in a declarative manner and specifies where to place cache objects depending on the status of the target record and its associated records. We also integrate a push notification system using WebSocket to immediately notify events on a specified table. We evaluate the performance of our system using a messaging service application between mobile appliances.
摘要：为边缘计算提供了数据框架，开发者可以在设备或用户中获得有效的数据传输。针对边缘计算，我们提出了分布式K-V存储平台以及清晰的数据分布管理方法。在这个平台，边缘服务器在统一的命名空间组织分布式K-V存储。为了使数据快速存取边缘计算的记录，记录的分配策略以及边缘服务器的缓存非常重要。我们平台提供的分布式对象能够动态改变主服务器，并根据用户定义的规则分配缓存对象。规则以陈述的方式被定义，列举在哪里放置缓存对象取决于目标记录的状态以及相关的记录。我们也用网络套接字（WebSocket）在指定表上通知相关事件。我们用信息服务应用在移动应用上评估了系统的性能。
【数据复制置位算法】A novel replica placement algorithm for minimising communication cost in distributed storage platform
Abstract：
In large-scale distributed systems, replication service has been playing a critical role to improve the availability and reliability of user data. Conventionally, the existing replication services mainly concentrate on how many replicas are needed to maintain desirable availability and reliability rather than how to place replicas on the most suitable storage nodes. As a result, the communication-related costs when accessing data are significantly increased, which in turn degrades the execution performance of user applications. In this paper, we propose a novel replica placement algorithm which is designed to minimise the communication cost when accessing or managing replicas in a large-scale storage platform. In the proposed algorithm, the replica placement problem is formulised a classical multi-knapsack problem, and two heuristic metrics are introduced to obtain the sub-optimal solution of this problem. A lot of experiments are conducted to investigate the performance of the proposed algorithm. The experimental results indicate that our replica placement algorithm outperforms many existing approaches in terms of different performance metrics. In addition, the proposed algorithm can also significantly improve the execution efficiency for data-intensive applications, which are very common in nowadays large-scale distributed systems, such as grid and cloud. Keywords: data replication, replica service, distributed storage, cloud computing
摘要：在大规模分布式系统里，复制服务对改善用户数据的可靠性和可获得性起着重要的作用。照惯例，现存的复制服务主要集中于，有多少副本需要维持要求的可获得性和可靠性，而不是如何将副本置于更合适的存储代码里。因此，访问数据时通信相关的花销显著增加，这反过来降低了用户应用的执行性能。在论文中，我们提出了新型副本置位算法，旨在在大规模存储平台中存取和管理数据副本时，能最小化通信开销。在提出的算法中，副本置位问题被抽象成一个多背包问题，引入了两个启发式的度量来得到次优的解决方案。实施了大量的实验来调查该算法的性能。实验结果表明，在不同性能度量下，本算法优于许多现存的方法。除此之外，本算法能显著提高数据密集应用的执行效率，这些应用在当代大规模分布式系统中非常普遍，例如网状和云状。
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