Tuesday 29 August 2017

B-Tech IEEE Projects | A Secure and Dynamic Multi keyword Ranked Search Scheme over Encrypted Cloud Data

Cloud Technologies Hyderabad

A Secure and Dynamic Multi keyword Ranked Search Scheme over Encrypted Cloud Data

Implementation Video Of A Secure and Dynamic Multi keyword Ranked Search Scheme over Encrypted Cloud Data

IEEE 2016-2017 Java Projects

SYSTEM REQUIREMENTS HARDWARE REQUIREMENTS:
Hardware : Pentium Speed : 1.1 GHz RAM : 1GB Hard Disk : 20 GB
SOFTWARE REQUIREMENTS:
Operating System : Windows Family Technology : Java and J2EE Web Technologies : Html, JavaScript, CSS Web Server : Apache Tomcat 7.0/8.0 Database : My SQL 5.5 or Higher UML's : StarUml Java Version : JDK 1.7 or 1.8
Implemented by
Development team :

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811

M-Tech Projects | Smart Crawler A Two - stage Crawler for Efficiently Harvesting Deep Web Interfaces

Cloud Technologies Hyderabad

Smart Crawler A Two - stage Crawler for Efficiently Harvesting Deep Web Interfaces

Implementation Video in Java From Cloud Technologies

IEEE 2016 2017 Java Projects

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811

M-Tech Cloud Computing Project | DiploCloud: Efficient and Scalable Managementof RDF Data in the Cloud

Cloud Technologies Hyderabad

DiploCloud: Efficient and Scalable Managementof RDF Data in the Cloud

Implementation Video in Java From Cloud Technologies

IEEE 2016 2017 Java Projects

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811

M-Tech IEEE 2016 Project | Privacy-Preserving Location Sharing Servicesfor Social Networks

Cloud Technologies Hyderabad

Privacy-Preserving Location Sharing Servicesfor Social Networks

Implementation Video in Java From Cloud Technologies

IEEE 2016-2017 Java Projects

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811

Sunday 27 August 2017

M-IEEE 2017 Projects | SociRank: Identifying and Ranking Prevalent News Topics Using Social Media Factors

Cloud Technologies Hyderabad

SociRank: Identifying and Ranking Prevalent News Topics Using Social Media Factors

Implementation Video in Java From Cloud Technologies

IEEE 2017 Java Projects

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811

B-Tech Main Projects | SociRank: Identifying and Ranking Prevalent News Topics Using Social Media Factors

Cloud Technologies Hyderabad

SociRank: Identifying and Ranking Prevalent News Topics Using Social Media Factors

Abstract:

Mass media sources, specifically the news media, have traditionally informed us of daily events. In modern times, social media services such as Twitter provide an enormous amount of user-generated data, which have great potential to contain informative news-related content. For these resources to be useful, we must find a way to filter noise and only capture the content that, based on its similarity to the news media, is considered valuable. However, even after noise is removed, information overload may still exist in the remaining data—hence, it is convenient to prioritize it for consumption. To achieve prioritization, information must be ranked in order of estimated importance considering three factors. First, the temporal prevalence of a particular topic in the news media is a factor of importance, and can be considered the media focus (MF) of a topic. Second, the temporal prevalence of the topic in social media indicates its user attention (UA). Last, the interaction between the social media users who mention this topic indicates the strength of the community discussing it, and can be regarded as the user interaction (UI) toward the topic. We propose an unsupervised framework—SociRank—which identifies news topics prevalent in both social media and the news media, and then ranks them by relevance using their degrees of MF, UA, and UI. Our experiments show that SociRank improves the quality and variety of automatically identified news topics.

Existing System

Historically, knowledge that apprises the general public of daily events has been provided by mass media sources, specifically the news media. The news media presents professionally verified occurrences or events, while social media presents the interests of the audience in these areas, and may thus provide insight into their popularity. Unfortunately, filter noise and only capture the content that, based on its news media, and social media is very difficult. However, even after noise is removed, information overload may still exist in the remaining data—hence, it is difficult to prioritize.

Disadvantages: 1. Hard to find a way to filter news from noisy. 2. High computational demand to prioritize.

Proposed System

We propose an unsupervised system SociRank which effectively identifies news topics that are prevalent in both social media and the news media, and then ranks them by relevance using their degrees of MF, UA, and UI. Even though this paper focuses on news topics. News media sources are considered reliable because they are published by professional journalists, who are held accountable for their content. On the other hand, the Internet, being a free and open forum for information exchange, has recently seen a fascinating phenomenon known as social media. In social media, regular, non-journalist users are able to publish unverified content and express their interest in certain events. Consolidated, filtered, and ranked news topics from both professional news providers and individuals have several benefits. The most evident use is the potential to improve the quality and coverage of news recommender systems or Web feeds, adding user popularity feedback.

Advantages: 1. We can find a way to filter noise and only capture the news. 2. We can filter the news based on topic. 3. Main use potential to improve the quality and coverage of news recommender systems.
SYSTEM REQUIREMENTS HARDWARE REQUIREMENTS:
Hardware : Pentium Speed : 1.1 GHz RAM : 1GB Hard Disk : 20 GB
SOFTWARE REQUIREMENTS:
Operating System : Windows Family Technology : Java and J2EE Web Technologies : Html, JavaScript, CSS Web Server : Apache Tomcat 7.0/8.0 Database : My SQL 5.5 or Higher UML's : StarUml Java Version : JDK 1.7 or 1.8
Implemented by
Development team :

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811

IEEE 2017-2018 Java Projects | M-Tech IEEE 2017-2018 Projects | B-Tech Main Projects

Cloud Technologies Hyderabad

IEEE 2017-2018 Java Projects | M-Tech IEEE 2017-2018 Projects | B-Tech Main Projects

IEEE-JAVA-2017-2018 Data Mining Projects

Point-of-interest Recommendation for Location Promotion in Location-based Social Networks

Personal Web Revisitation by Context and Content Keywords with Relevance Feedback

SociRank: Identifying and Ranking Prevalent News Topics Using Social Media Factors

IEEE-JAVA-2017-2018 Cloud Computing Projects Using Amazon Web Services

Identity-Based Data Outsourcing With Comprehensive Auditing in Clouds

RAAC: Robust and Auditable Access Control With Multiple Attribute Authorities for Public Cloud Storage

Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud

A Novel Efficient Remote Data Possession Checking Protocol in Cloud Storage

Fast Phrase Search for Encrypted Cloud Storage

Identity-based Remote Data Integrity Checking with Perfect Data Privacy Preserving for Cloud Storage

Practical Privacy-Preserving Content-Based Retrieval in Cloud Image Repositories

Provably Secure Key-Aggregate Cryptosystems with Broadcast Aggregate Keys for Online Data Sharing on the Cloud

Secure k-NN Query on Encrypted Cloud Data with Multiple Keys

TAFC: Time and Attribute Factors Combined Access Control for Time-Sensitive Data in Public Cloud

Saturday 26 August 2017

Paper Publishing In UGC Approved Journals For M-Tech Students

Cloud Technologies Hyderabad

Paper Publishing In UGC Approved Journals For M-Tech Students

We Providing papers for publication in all areas of engineering, science and technology.

We Impact Factor 4 & Above

With Following Support

1-Modified Paper
2-Acceptance Letter
3-Soft Copy of Paper
4-Online Paper
5-Soft Copy of Certificates
6-Hard Copy of Certificates

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811

Friday 25 August 2017

CSE Main Projects | Point-of-interest Recommendation for Location Promotion in Location-based Social Networks

Cloud Technologies Hyderabad

Point-of-interest Recommendation for Location Promotion in Location-based Social Networks

Implementation Video in Java From Cloud Technologies

IEEE 2017 Java Projects

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811

M-Tech IEEE Project| Personal Web Revisitation by Context and Content Keywords with Relevance Feedback

Cloud Technologies Hyderabad

Personal Web Revisitation by Context and Content Keywords with Relevance Feedback

Implementation Video in Java From Cloud Technologies

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811

Wednesday 23 August 2017

B-Tech main Projects | Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud

Attribute-Based Storage Supporting Secure Deduplication of Encrypted Data in Cloud

Abstract

Attribute-based encryption (ABE) has been widely used in cloud computing where a data provider outsources his/her encrypted data to a cloud service provider, and can share the data with users possessing specific credentials (or attributes). However, the standard ABE system does not support secure de-duplication, which is crucial for eliminating duplicate copies of identical data in order to save storage space and network bandwidth. In this paper, we present an attribute-based storage system with secure de-duplication in a hybrid cloud setting, where a private cloud is responsible for duplicate detection and a public cloud manages the storage. Compared with the prior data de-duplication systems, our system has two advantages. Firstly, it can be used to confidentially share data with users by specifying access policies rather than sharing decryption keys. Secondly, it achieves the standard notion of semantic security for data confidentiality while existing systems only achieve it by defining a weaker security notion. In addition, we put forth a methodology to modify a ciphertext over one access policy into ciphertexts of the same plaintext but under other access policies without revealing the underlying plaintext.

Existing System

In existing system a data provider Bob intends to upload a file M to the cloud, and share M (file data) with users having certain credentials. In order to do so, Bob encrypts M under an access policy A over a set of attributes, and uploads the corresponding ciphertext to the cloud, such that only users whose sets of attributes satisfying the access policy can decrypt the ciphertext. Later, another data provider Alice, uploads a ciphertext for the same underlying file M but ascribed to a different access policy A0. Since the file is uploaded in an encrypted form, the cloud is not able to discern that the plaintext corresponding to Alice’s ciphertext is the same as that corresponding to Bob’s, and will store M twice. Obviously, such duplicated storage wastes storage space and communication bandwidth.

Proposed System

In this paper, we present an attribute-based storage system which employs ciphertext-policy attribute-based encryption (CP-ABE) and supports secure deduplication. In the proposed attributed-based system, the same file could be encrypted to different ciphertexts associated with different access policies, storing only one ciphertext of the file means that users whose attributes satisfy the access policy of a discarded ciphertext (but not that of the stored ciphertext) will be denied to access the data that they are entitled to. To overcome this problem, we equip the private cloud with another capability named ciphertext regeneration. For a ciphertext c of a plaintext M with access policy A, the private cloud will be provided with a trapdoor key which is generated along with the ciphertext c by a data provider. The private cloud can use the trapdoor key to convert the ciphertext c with access policy A to a new ciphertext C with another access policy A0 without knowing the underlying message M. Thus, if two data providers happen to upload two ciphertexts corresponding to the same file but under different access policies A and A0, the private cloud can regenerate a ciphertext for the same underlying file with an access policy A UA0 using the corresponding trapdoor key and then store the new ciphertext instead of the old one in the public cloud.

Modules:

Data Provider:
A data provider wants to outsource his/her data to the cloud and share it with users possessing certain credentials.
Attribute Authority (AA):
In this system Attribute Authority can generate first Public Key PK and Master Key MK as well The authority executes the algorithm which inputs a set of attributes S(S ⊆ A˜) and creates a Secret Key SK and these keys can be send to authorized User‘s.
Cloud:
The cloud consists of a public cloud which is in charge of data storage and a private cloud which performs certain computation such as tag checking.
User:
At the user side, each user can download an item, and decrypt the ciphertext with the attribute-based private key generated by the AA if this user’s attribute set satisfies the access structure. Each user checks the correctness of the decrypted message using the label, and accepts the message if it is consistent with the label.

SYSTEM REQUIREMENTS HARDWARE REQUIREMENTS:
Hardware : Pentium Speed : 1.1 GHz RAM : 1GB Hard Disk : 20 GB
SOFTWARE REQUIREMENTS:
Operating System : Windows Family Technology : Java and J2EE Web Technologies : Html, JavaScript, CSS Web Server : Apache Tomcat 7.0/8.0 Database : My SQL 5.5 or Higher UML's : StarUml Java Version : JDK 1.7 or 1.8
Implemented by
Development team :

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811

IEEE 2017 B-Tech Main & M-Tech Project | Point-of-interest Recommendation for Location Promotion in Location-based Social Networks

Point-of-interest Recommendation for Location Promotion in Location-based Social Networks

Abstract

With the wide application of location-based social networks (LBSNs), point-of-interest (POI) recommendation has become one of the major services in LBSNs. The behaviors of users in LBSNs are mainly checking in POIs, and these checking in behaviors are influenced by user’s behavior habits and his/her friends. In social networks, social influence is often used to help businesses to attract more users. Each target user has a different influence on different POI in social networks. This paper selects the list of POIs with the greatest influence for recommending users. Our goals are to satisfy the target user’s service need, and simultaneously to promote businesses’ locations (POIs). This paper defines a POI recommendation problem for location promotion. Additionally, we use sub modular properties to solve the optimization problem. At last, this paper conducted a comprehensive performance evaluation for our method using two real LBSN datasets. Experimental results show that our proposed method achieves significantly superior POI recommendations comparing with other state-of-the-art recommendation approaches in terms of location promotion.

Existing System

Recently, many researchers have been engaged in location-aware services. In LBSNs, users can post comments on locations or activities, upload photos, and share check-in locations in which users are interested with friends. These locations are called points-of-interest (POIs). Currently, POI recommendation has become one of main location-aware services in LBSNs. POI recommendation approaches mostly involve recommending users with some locations in which users may be interested based on users’ characters, preferences, and behavioral habits. Through the detailed analysis above, we observe traditional POI recommendations rarely focus on the effect of social relationships for businesses location promotion through the POI recommendation process.

Disadvantages: • No Concept for on the location promotion in LBSNs. • Helps only for business people not for users.

Proposed System

In view of POIs, POIs (e.g.restaurants, hotel, markets) have to explore checking-in records to attract more users to visit; more users (e.g., friends of users that checked in these POIs) will be influenced to check in these locations. In this paper, we regard the influence on the business as a maximization location promotion problem. The essential goals of recommendation system are to satisfy users’ service demands and merchants’ advertising needs. this paper proposes POI recommendation method for promoting POIs. Our proposed method is not only a tool for businesses to use to promote their products and attract more customers to visit their stores, but also recommends users with some POI’s satisfying users preferences.

Advantages: • We propose a novel point-of-interest recommendation problem, and its goal is to promote the businesses’ locations ( POIs ). • We define the user’s IS under special POI categories in an entire social network, and model user mobility to describe the geographical influence between user. Few Points:

In this paper, focus on POI recommendation to social user based on his friends and friends of friends instead of unknown recommendation. Main Application collects check in data with geo properties. Like user from his location move to POI , PGu,v(l) tradeoff between geographical influence. And Target user to another user relation, PTu,v(l) semantic influence b/w u and v. POI recommendation approaches mostly involve recommending users with some locations in which users may be interested based on users’ characters, preferences. Like Facebook no suggest you some business locations according to your interests.

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811

Tuesday 22 August 2017

M-Tech IEEE 2017 Project | Personal Web Revisitation by Context and Content Keywords with Relevance Feedback

Personal Web Revisitation by Context and Content Keywords with Relevance Feedback

Abstract

Getting back to previously viewed web pages is a common yet uneasy task for users due to the large volume of personally accessed information on the web. This paper leverages human’s natural recall process of using episodic and semantic memory cues to facilitate recall, and presents a personal web revisitation technique called WebPagePrev through context and content keywords. Underlying techniques for context and content memories’ acquisition, storage, decay, an utilization for page re-finding are discussed. A relevance feedback mechanism is also involved to tailor to individual’s memory strength and revisitation habits. Our dynamic management of context and content memories including decay and reinforcement strategy can mimic users’ retrieval and recall mechanism. Among time, location, and activity context factors in WebPagePrev, activity is the best recall cue, and context + content based re-finding delivers the best performance, compared to context based re-finding and content based re-finding.

Existing System

In the literature, a number of techniques and tools like bookmarks, history tools, search engines, etc systems have been developed to support personal web revisitation. In existing search engine, and fetched relevant previously viewed results from its cache. The newly available results were then merged with the previously viewed results to create a list that supported intuitive re-finding and contained new information. For Ex: History Tools. History tools of web browsers maintain user’s accessed URLs chronologically according to visit time (e.g., today, yesterday, last week, etc.), and accessed page titles and contents. Search Engines. How search engines are used for re-finding previously found search results. It explored the differences between queries that had substantial/minimal changes between the previous query and the revisit query.

Disadvantages: • No search for web revisitation. • Depends on only time and date.

Proposed System

Inspired by the psychological findings, this paper explores how to leverage our natural recall process of using episodic and semantic memory cues to facilitate personal web revisitation. We present a personal web revisitation technique, called WebPagePrev, that allows users to get back to their previously focused pages through access context and page content keywords. Underlying techniques for context and content memories’ acquisition, storage, and utilization for web page recall are discussed. Preparation for web revisitation. When a user accesses a web page, which is of potential to be revisited later by the user (i.e., page access time is over a threshold), the context acquisition and management module captures the current access context (i.e., time, location, activities inferred from the currently running computer programs) into a probabilistic context tree.
Advantages: • New technology for personal web revisitation. • Depend on both context and content keywords.

Cloud Technologies

www.cloudstechnologies.in

Contact : 8121953811, 040-65511811