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Anonymization of centralized and distributed social networks by sequential cl...IEEEFINALYEARPROJECTS
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Anonymization techniques are used to ensure the privacy preservation of the data owners, especially for personal and sensitive data. While in most cases, data reside inside the database management system; most of the proposed anonymization techniques operate on and anonymize isolated datasets stored outside the DBMS. Hence, most of the desired functionalities of the DBMS are lost, e.g., consistency, recoverability, and efficient querying. In this paper, we address the challenges involved in enforcing the data privacy inside the DBMS. We implement the k-anonymity algorithm as a relational operator that interacts with other query operators to apply the privacy requirements while querying the data. We study anonymizing a single table, multiple tables, and complex queries that involve multiple predicates. We propose several algorithms to implement the anonymization operator that allow efficient non-blocking and pipelined execution of the query plan. We introduce the concept of k-anonymity view as an abstraction to treat k-anonymity (possibly, with multiple k preferences) as a relational view over the base table(s). For non-static datasets, we introduce the materialized k-anonymity views to ensure preserving the privacy under incremental updates. A prototype system is realized based on PostgreSQL with extended SQL and new relational operators to support anonymity views. The prototype system demonstrates how anonymity views integrate with other privacy- preserving components, e.g., limited retention, limited disclosure, and privacy policy management. Our experiments, on both synthetic and real datasets, illustrate the performance gain from the anonymity views as well as the proposed query optimization techniques under various scenarios.
Lions, zebras and Big Data AnonymizationKai Xin Thia
In a recent safari trip to Tanzania, East Africa, I observed that lions are not interested in attacking the human visitors at all. What is the secret to the safari's (non existent) security measures for their visitors? How do we determine the optimum tradeoff between enjoying the safari and safety? How can we quantify the risk? And ultimately, how can we apply these lessons + data anonymization techniques to Big Data?
Problems in Technology to Use Anonymized Personal DataHiroshi Nakagawa
Privacy is a big issue these days. Legally, EU data protection directive will be revised, Google was defeated in EU court and forced to erase data link uopn user's request.
However, we are facing various technical problems to be solved even if limiting to anonyumisation or k-anonymity. In this slide, we describe three of these problmes.
Anonymization of centralized and distributed social networks by sequential cl...IEEEFINALYEARPROJECTS
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Anonymization techniques are used to ensure the privacy preservation of the data owners, especially for personal and sensitive data. While in most cases, data reside inside the database management system; most of the proposed anonymization techniques operate on and anonymize isolated datasets stored outside the DBMS. Hence, most of the desired functionalities of the DBMS are lost, e.g., consistency, recoverability, and efficient querying. In this paper, we address the challenges involved in enforcing the data privacy inside the DBMS. We implement the k-anonymity algorithm as a relational operator that interacts with other query operators to apply the privacy requirements while querying the data. We study anonymizing a single table, multiple tables, and complex queries that involve multiple predicates. We propose several algorithms to implement the anonymization operator that allow efficient non-blocking and pipelined execution of the query plan. We introduce the concept of k-anonymity view as an abstraction to treat k-anonymity (possibly, with multiple k preferences) as a relational view over the base table(s). For non-static datasets, we introduce the materialized k-anonymity views to ensure preserving the privacy under incremental updates. A prototype system is realized based on PostgreSQL with extended SQL and new relational operators to support anonymity views. The prototype system demonstrates how anonymity views integrate with other privacy- preserving components, e.g., limited retention, limited disclosure, and privacy policy management. Our experiments, on both synthetic and real datasets, illustrate the performance gain from the anonymity views as well as the proposed query optimization techniques under various scenarios.
Lions, zebras and Big Data AnonymizationKai Xin Thia
In a recent safari trip to Tanzania, East Africa, I observed that lions are not interested in attacking the human visitors at all. What is the secret to the safari's (non existent) security measures for their visitors? How do we determine the optimum tradeoff between enjoying the safari and safety? How can we quantify the risk? And ultimately, how can we apply these lessons + data anonymization techniques to Big Data?
Problems in Technology to Use Anonymized Personal DataHiroshi Nakagawa
Privacy is a big issue these days. Legally, EU data protection directive will be revised, Google was defeated in EU court and forced to erase data link uopn user's request.
However, we are facing various technical problems to be solved even if limiting to anonyumisation or k-anonymity. In this slide, we describe three of these problmes.
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Anonymization of centralized and distributed social networks by sequential cl...ecway
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BIG DATA SANITIZATION AND CYBER SITUATIONALAWARENESS: A NETWORK TELESCOPE PE...Nexgen Technology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
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Near SBI ATM,
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Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Scalable face image retrieval using attribute enhanced sparse codewordsIEEEFINALYEARPROJECTS
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Scalable face image retrieval using attribute enhanced sparse codewordsIEEEFINALYEARPROJECTS
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https://utilitasmathematica.com/index.php/Index
Our journal has academic and professional communities fosters collaboration and knowledge sharing. When all voices are heard and respected, it strengthens the collective capabilities of the statistical community.
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
In planet-scale deployments, the Operation and Maintenance (O&M) of cloud platforms cannot be done any longer manually or simply with off-the-shelf solutions. It requires self-developed automated systems, ideally exploiting the use of AI to provide tools for autonomous cloud operations. This talk will explain how deep learning, distributed traces, and time-series analysis (sequence analysis) can be used to effectively detect anomalous cloud infrastructure behaviors during operations to reduce the workload of human operators. The iForesight system is being used to evaluate this new O&M approach. iForesight 2.0 is the result of 2 years of research with the goal to provide an intelligent new tool aimed at SRE cloud maintenance teams. It enables them to quickly detect and predict anomalies thanks to the use of artificial intelligence when cloud services are slow or unresponsive.
Anonymization of centralized and distributed social networks by sequential cl...ecway
Final Year IEEE Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE Projects, Academic Final Year IEEE Projects 2013, Academic Final Year IEEE Projects 2014, IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects, 2013 IEEE JAVA, .NET Projects in Chennai, 2013 IEEE JAVA, .NET Projects in Trichy, 2013 IEEE JAVA, .NET Projects in Karur, 2013 IEEE JAVA, .NET Projects in Erode, 2013 IEEE JAVA, .NET Projects in Madurai, 2013 IEEE JAVA, .NET Projects in Salem, 2013 IEEE JAVA, .NET Projects in Coimbatore, 2013 IEEE JAVA, .NET Projects in Tirupur, 2013 IEEE JAVA, .NET Projects in Bangalore, 2013 IEEE JAVA, .NET Projects in Hydrabad, 2013 IEEE JAVA, .NET Projects in Kerala, 2013 IEEE JAVA, .NET Projects in Namakkal, IEEE JAVA, .NET Image Processing, IEEE JAVA, .NET Face Recognition, IEEE JAVA, .NET Face Detection, IEEE JAVA, .NET Brain Tumour, IEEE JAVA, .NET Iris Recognition, IEEE JAVA, .NET Image Segmentation, Final Year JAVA, .NET Projects in Pondichery, Final Year JAVA, .NET Projects in Tamilnadu, Final Year JAVA, .NET Projects in Chennai, Final Year JAVA, .NET Projects in Trichy, Final Year JAVA, .NET Projects in Erode, Final Year JAVA, .NET Projects in Karur, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Tirunelveli, Final Year JAVA, .NET Projects in Madurai, Final Year JAVA, .NET Projects in Salem, Final Year JAVA, .NET Projects in Tirupur, Final Year JAVA, .NET Projects in Namakkal, Final Year JAVA, .NET Projects in Tanjore, Final Year JAVA, .NET Projects in Coimbatore, Final Year JAVA, .NET Projects in Bangalore, Final Year JAVA, .NET Projects in Hydrabad, Final Year JAVA, .NET Projects in Kerala, Final Year JAVA, .NET IEEE Projects in Pondichery, Final Year JAVA, .NET IEEE Projects in Tamilnadu, Final Year JAVA, .NET IEEE Projects in Chennai, Final Year JAVA, .NET IEEE Projects in Trichy, Final Year JAVA, .NET IEEE Projects in Erode, Final Year JAVA, .NET IEEE Projects in Karur, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Tirunelveli, Final Year JAVA, .NET IEEE Projects in Madurai, Final Year JAVA, .NET IEEE Projects in Salem, Final Year JAVA, .NET IEEE Projects in Tirupur, Final Year JAVA, .NET IEEE Projects in Namakkal, Final Year JAVA, .NET IEEE Projects in Tanjore, Final Year JAVA, .NET IEEE Projects in Coimbatore, Final Year JAVA, .NET IEEE Projects in Bangalore, Final Year JAVA, .NET IEEE Projects in Hydrabad, Final Year JAVA, .NET IEEE Projects in Kerala, Final Year IEEE MATLAB Projects, Final Year Projects, Academic Final Year Projects, Academic Final Year IEEE MATLAB Projects, Academic Final Year IEEE MATLAB Projects 2013, Academic Final Year IEEE MATLAB Projects 2014, IEEE MATLAB Projects, 2013 IEEE MATLAB Projects, 2013 IEEE MATLAB Projects in Chennai, 2013 IEEE MATLAB Projects in Trichy, 2013 IEEE MATLAB Projects in Karur, 2013 IEEE MATLAB Projects in Erode, 2013 IEEE MATLAB Projects in Madurai, 2013 IEEE MATLAB
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BIG DATA SANITIZATION AND CYBER SITUATIONALAWARENESS: A NETWORK TELESCOPE PE...Nexgen Technology
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...
Protecting sensitive labels in social network data anonymization
1. Protecting Sensitive Labels in Social Network Data Anonymization
ABSTRACT:
Privacy is one of the major concerns when publishing or sharing social network data for social
science research and business analysis. Recently, researchers have developed privacy models
similar to k-anonymity to prevent node reidentification through structure information. However,
even when these privacy models are enforced, an attacker may still be able to infer one’s private
information if a group of nodes largely share the same sensitive labels (i.e., attributes). In other
words, the label-node relationship is not well protected by pure structure anonymization
methods. Furthermore, existing approaches, which rely on edge editing or node clustering, may
significantly alter key graph properties. In this paper, we define a k-degree-l-diversity
anonymity model that considers the protection of structural information as well as sensitive
labels of individuals. We further propose a novel anonymization methodology based on adding
noise nodes. We develop a new algorithm by adding noise nodes into the original graph with
the consideration of introducing the least distortion to graph properties. Most importantly, we
provide a rigorous analysis of the theoretical bounds on the number of noise nodes added and
their impacts on an important graph property. We conduct extensive experiments to evaluate the
effectiveness of the proposed technique.
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Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmail.com
2. EXISTING SYSTEM:
Recently, much work has been done on anonymizing tabular microdata. A variety of privacy
models as well as anonymization algorithms have been developed (e.g., kanonymity, l-diversity,
t-closeness. In tabular microdata, some of the nonsensitive attributes, called quasi identifiers,
can be used to reidentify individuals and their sensitive attributes. When publishing social
network data,graph structures are also published with corresponding social relationships. As a
result, it may be exploited as a new means to compromise privacy.
DISADVANTAGES OF EXISTING SYSTEM:
The edge-editing method sometimes may change the distance properties substantially by
connecting two faraway nodes together or deleting the bridge link between two
communities.
Mining over these data might get the wrong conclusion about how the salaries are
distributed in the society. Therefore, solely relying on edge editing may not be a good
solution to preserve data utility.
PROPOSED SYSTEM:
We propose a novel idea to preserve important graph properties, such as distances between
nodes by adding certain “noise” nodes into a graph. This idea is based on the following key
observation.
In Our proposed system, privacy preserving goal is to prevent an attacker from reidentifying a
user and finding the fact that a certain user has a specific sensitive value. To achieve this goal,
we define a k-degree-l-diversity (KDLD) model for safely publishing a labeled graph, and then
develop corresponding graph anonymization algorithms with the least distortion to the
properties of the original graph, such as degrees and distances between nodes.
3. ADVANTAGES OF PROPOSED SYSTEM:
We combine k-degree anonymity with l-diversity to prevent not only the reidentification
of individual nodes but also the revelation of a sensitive attribute associated with each
node.
We propose a novel graph construction technique which makes use of noise nodes to
preserve utilities of the original graph. Two key properties are considered: 1) Add as few
noise edges as possible; 2) Change the distance between nodes as less as possible.
We present analytical results to show the relationship between the number of noise nodes
added and their impacts on an important graph property.
SYSTEM ARCHITECTURE:
10. SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
Processor - Pentium –IV
Speed - 1.1 Ghz
RAM - 256 MB(min)
Hard Disk - 20 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
SOFTWARE CONFIGURATION:-
Operating System : Windows XP
Programming Language : JAVA
Java Version : JDK 1.6 & above.
REFERENCE:
Mingxuan Yuan, Lei Chen, Member, IEEE, Philip S. Yu, Fellow, IEEE, and Ting Yu-
“Protecting Sensitive Labels in Social Network Data Anonymization”-IEEE
TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 3,
MARCH 2013.