Nt1330 Unit 7 Lab 1

The eighth lab was about the RC Circuit Analysis. The purpose of the experiment is to examine the resistor, capacitor circuit for different voltage inputs and study circuits charging and discharging behavior depending on the input function. To do the lab, first set up the circuit as shown in the figure. The first part of this lab will be analyzed a resistor and capacitor circuit with a DC voltage input (steady state). When we applied the DC voltage, the output behavior shown on the oscilloscope was just an impulse and rest of the graph was a steady line. We clearly saw that the 5 volts drop across the capacitor which was the steady line. When in an RC circuit connected to a DC voltage source, the current decreases from its initial value of

energy ($\omega$). Solid lines are CRPA cross sections and dashed lines are HF cross sections.

\item [I-] $\textit{Trapdoor Generator}$: To retrieve only the documents containing keywords $Q$, the data user $U$ has to ask the $O$ for public key $O_{pub}$ to generate trapdoors; If $O$ is offline these owners' data can't be retrieved in time. If not, $U$ will get the public key $O_{pub}$ and create one trapdoor for a conjunctive keyword set $Q=\{q_1,q_2,...,q_l\}$, using $\textsf{TrapdoorGen}(Q, PP, PR$) algorithm. Firstly, the data user combines the conjunctive queries to make them look like one query, $Tq=\{q_1\| q_2\|...\| q_l\}$, then $U$ will compute the trapdoor of the search request of concatenated conjunctive keywords $\textit{Tq}$ under his private key $b$, $Tw=H_1(Tq)^b \in \mathbb{G}_1 $. Finally, $U$ submits $Tw$ to the cloud server.

Lab Three repeats much of Lab Two by setting up the same core services, DNS and DHCP, but adds Microsoft’s Active Directory role to the server. The lab introduces the student to administrating these services in a Graphical based, Windows environment.

Perform these steps for each & every cluster. SELECT_CLUSTERHEAD (n, S) Begin: Step1. Let we have a set S of n nodes in a cluster viz. S= {S1, S2, S3… Sn} Step2.

The data privacy issue is a main concern in cloud storage system, so the sensitive data is encrypted by the owner before outsourcing onto the cloud, and data users retrieve the interested data by encrypted search scheme. In MCS, the modern mobile devices are encountering with many of the security threats as PCs, and various traditional data encryption methods are imported in MCS [5], [6]. However, mobile cloud storage system incurs new challenges over the traditional encrypted search schemes, in consideration of the limited computing and battery capacities of mobile device, as well as data sharing and accessing approaches through wireless communication. Therefore, a suitable and efficient encrypted search scheme is mandatory for MCS. Generally speaking, the mobile cloud storage is in great need of the bandwidth and energy efficiency for data

Cloud services exhibits of online file storage, social networking sites, web mail and online business applications. These are the services which changed the face of networking. By this facility we can upload,

Cloud because of its wide range of applications it allows users to store data their data remotely in the cloud and enjoy the on-demand high quality cloud applications and reveal burden from the local storage, cost and maintenance. In this according to the user’s perspective, including both individuals (private) and enterprises like companies appealing the cloud benefits by storing data remotely into the cloud in a flexible on-demand manner and relief of the burden of storage management along with this he/she can also enjoy the universal data access which dependent geographical locations and avoidance of the capital expenditure, software, hardware and personnel management and maintenances and so on.

The volume and density of streaming data have also been rapidly growing. Appropriate indexing approaches are essential to handle fast incoming data and to process continuous flow of queries. A new indexed structure is proposed to reduce the space cost and speed up the retrieval from data storage. ACBSD (Adaptive Clustering Based Stream Data) is proposed to index and retrieve streaming data efficiently. ACBSD-tree is proposed which aims to address the three main challenges in data indexing (1) scalable insert, (2) fast search, and (3) scalable deletion. The tree-based indexing structure requires much less space than linear structure.

To study a system which will help to secure data stored on the cloud storage system. This system may be helpful to for users who wants secure thier data as well as for companies who wants to give access permission to limited data to their employees and secure their data in cloud storage system.

With the fast computers and signal processors available in the 2000s, cloud computing become the most common form of data storage and generally, is used because it is not only the most versatile method, but also the cheapest.

Thinking about the practical trouble of privacy retaining records sharing system primarily based on public cloud storage which calls for a statistics proprietor to distribute a big wide variety of keys to users to permit them to access his/her files, we for the first time endorse the idea of key-mixture searchable encryption (KASE) and assemble a concrete KASE scheme. Both analysis and evaluation results verify that our work can provide an powerful solution to building practical records sharing system based totally on public cloud garage.

I was always inspired by the idea of storing large amounts of structured and unstructured data. Therefore I chose to finish my concentration in Data Management.

: Cloud computing is the well-known model used for storing huge amount of data over the internet and provides the convenient mechanisms to access the information. Since it is keeping up enormous measures of assets, its protection and security are the major issues. The cloud administration suppliers are not trusted and unethical, so information is to be secured. Still, some information might be accessible that the data proprietor does not wish for progress data to the cloud unless query confidentiality and data privacy are assured. On the other hand, protected query processing services have to grant efficient query processing and drastically reduce the internal workload to fully understand the benefits. “Random space perturbation (RASP) processing” method provides security and various query processing services to provide confidentiality in the cloud. The (K-Nearest Neighbour) KNN-R algorithm is used here to convert the range query to the KNN query. Users have been certified by using the randomly generated key value provided by the administrator subsequent to successful registration by the client thus maintaining privacy. Queries from users are retrieved within the least period of time i.e., less than a second. In future using RASP statistics and KNN queries to investigate supplementary applications of RASP perturbation for protected data concentrated computing in the cloud.

Cloud computing has become the most common phenomenon in the recent years. More and more cloud services have flourished all around the world such as computing resource, storage space outsourcing and different kinds of software applications. For many reasons like low cost, efficiency, convenience, better connectivity and etc., user often stores his data on remote servers. Since more servers are public, there exist a lot of risks for the data in the transition process, the user ensures the privacy of his data by storing it in encrypted form, then he can search the encrypted data and retrieve it. The first effort of searching encrypted data by keyword was tackled by Song, Wagner and Perrig cite{s1}. To securely search over encrypted data, searchable encryption schemes have been proposed in recent years cite{b2,b3,b5,c2,c4,g1,w2}, which can be divided into two schemes: symmetric searchable encryption (SSE) and asymmetric searchable encryption (ASE). To perform a search on a dataset, an user creates an index of keywords listed in the documents and later on executes the search on the index in a way that allows the server to retrieve the documents contain a certain keyword instead of retrieving all the encrypted documents back which is fully impractical solution in cloud computing scenarios. Recent refinements and extensions to this scheme are given in cite{g1, w2}.