The algorithm is executed by the owner to encrypt the plaintext of $D$ as follows:
\begin {enumerate}
\item [1:]for each document $D_i \in D$ for $i \in [1,n]$ do
\item [2:]encrypt the plaintext of $D_i$ using also $\textit{El Gamal}$ cipher under $\textit{O's}$ private key $a$ and $\textit{U's}$ public key $U_{pub}$ as $Enc_{D_i}= U_{pub}^a \times D_i $
\item [3:]end for
\item[4:] return $\textit{EncDoc}$
\end{enumerate}
\subsubsection{\textit{\textbf {Retrieval phase}}} Include three algorithms as detailed below:
\begin{enumerate}
\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.
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Then $S$ test $\textit{BF}$ in all $r$ locations, if all $r$ locations of all independent hash functions in $\textit{BF}$ are 1, the remote server returns the relevant encrypted file corresponding the $ID_i$ to $U$. In other words searchable index $I_D$ can be used to check set membership without leaking the set items, and for accumulated
The iBooks Author format is only able to be read in the iBooks ecosystem, and it can only be created in the iBooks Author program on a Macintosh computer.
Heating a popcorn. External: I put the popcorn bag in the microwave and heat it up, so the popcorn will be ready to eat. Internal: The heat that is emitted from the microwave will heat the corn shell, so that it will pop.
The complexity and memory requirements of the algorithm are in the order of $D_\mathcal{D}.{\rm N}$, denoted as $O(D_\mathcal{D}{\rm N})$. The algorithm become more effective when the minimum difference $\Delta Y_k$ is large between the transmission probability gains $Y_k$. Consequently, our algorithm finds an optimal solution coupled with linear complexity, when the network become more heterogeneous due to small ${\rm N}$. Our algorithm also finds the optimal solution with an increased complexity, when the network becomes more homogeneous or less heterogeneous due to increasing ${\rm N}$. The quantization precision ${\rm N}$ in Algorithm 1, is a physical quantity specified by the underlying network that elaborate the designing of quantization step. The total content size $H$ depends on the required content transmission rate $r_{c_i, d_i}$ and $Y_k$, whereas $Y_k \sigma$ are defined by the contact dynamics of the nodes in a network. However, if the network is difficult by the required level of transmission ratio, the values of $Y_k$ and the value of $\Delta Y_k$, such that $\rm N$ is too large, then the designer may have to comprise by reducing the desired level of transmission ratio to reduce $\rm N$. In results, the sub-optimal
Reduce time to access the required data: DDBMS allows to store copies of a data in multiple branches.
This Assignment had some difficulties for me. Originally, I had made notes on two conversations, wrote the transcripts for each and went on to arrange each in a separate poem. I really wasn’t feeling either of them. I went to the Internet to search out “Found Pome” and read some interesting information. I contacted Dr, Juchartz for clarification on this assignment. After he advised me, I took my notebook to a dinner and took notes on several conversations going on around me. I repeated the process of transcription and arranged the new conversation with the ones already arranged.
A. Setup: The setup phase takes input a security parameter . It selects a bilinear group of prime order p with b as generator, and bilinear map The universe attribute is . It selects for attribute n, , and a random exponent . The public key and master key is given by
For Evaluation (Chapter \ref{ch:evaluation}) of our approach, we needed to replicate different navigation requests. These navigation requests would follow the protocol described in Section \ref{protocol}. In order to replicate real-life scenarios, we wanted to find pair of OSM IDs which exist in all our different versions of maps. Additionally, to be more realistic we wanted the composition of such OSM ID pairs to follow certain rules which are explained in Section \ref{evaluationParameters}.
In this section, we discuss the implementation of XXX and evaluate the performance of our proposed algorithm with synthetic inputs in terms of 1) SFC request acceptance ratio, 2) backup resource consumed by requests and 3) running time. For comparison, we implement a baseline method, where for an SFC request which requires n VNF and has the availability requirement of _, we keep increasing the number of backup for each VNF on all selected physical machines until each VNF can have an availability of n p _. The statistics shown in this section are the average results.
In appearance CDStore, a incorporated multi-cloud packing account for manipulators to contract out gridlock data with steadfastness, security, in addition to cost efficiency guarantees. CDStore builds on an amplified secret chipping in design called convergent dispersal, which ropes deduplication by resources of deterministic contented derived hashes as inputs to surreptitious immersion. The design of CDStore, in addition to in particular, describe how it combines convergent dispersal with two-stage deduplication to achieve both bin addition towidth in addition to storage savings in addition to also be robust against side-channel attacks that can be launched by a malicious user on the client side. I exhibit via cost analysis that CDStore achieves
Big Data is creating great opportunities for businesses, companies and many large scale and small scale industries. Hadoop is an open-source cloud computing and big data framework, is increasingly used in the IT world. The rapid growth of Hadoop and Cloud Computing clearly indicates its importance as a Big Data enabling technology. Due to the loopholes of security mechanism, the security issues introduced through adaptation of this technology are also increasing. Hadoop services do not
In addition to delegation, remote file storage can be more than ever secure with Homomorphic encryption(HE), In a motivating example, consider a user that wants to run a keyword search on its entire set of encrypted data. Without HE, since the server can’t tell which documents contain the keyword, it would be forced to send the entire set of encrypted data back to the user, who could decrypt it and look for the keyword. With HE, however, the server can simply run the keyword search algorithm with the encrypted keyword and the set of encrypted data, and send an encrypted list of documents containing the keyword back to the
General computational soundness result for key exchange protocols with symmetric encryption has been performed in [19], along the lines of a chapter by Canetti and Herzog on protocols with public-key encryption.
In a typical database-backed applications the user queries are forwarded by the application server to the DBMS server which then executes the query over the database and returns the result back to the application server. CryptDB works by intercepting the queries sent by the application server to the DBMS server using a proxy server. The proxy server encrypt these queries and send the encrypted version to the DBMS server. The proxy server store the details about the current encryption layers of all columns in the database, a secret Master key used for encryption and the annotations designed by the application developer. CryptDB also includes User-defined functions (UDF) that help the DBMS server in removing the upper layers of onion for performing computations. The query for removing the upper layers will be designed and send to the DBMS server by the proxy server which then executes it with the help of the UDF.
The cloud needs certain sets of characteristics to enable the remote provisioning of scalable and measured resources in an effective manner. [3]
The evolution of distributed web based applications and cloud computing have generated the demand to store voluminous of big data in distributed databases efficiently to offer excessive availability and scalability to users. The new type of database resolves many new challenges especially in large-scale and high concurrency applications which are not present in relational database. These new sorts of databases are not relational by using explanations and hence they do not prop up whole SQL performance. As progressively insightful big data is being saved in NoSQL databases, it is essential to preserve higher security measures to ensure safe and trusted communication across the network. In this paper, we describe the security of NoSQL database against intruders which is growing rapidly. This paper also delineates probably the most prominent NoSQL databases and descriptions their security aspects and problems.