Sensitive Label Privacy Preservation with Anatomization for Data Publishing

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Sensitive Label Privacy Preservation with Anatomization for
Data Publishing
ABSTRACT:
Data in its original form, however, typically contain sensitive information about
individuals. Directly publishing raw data will violate the privacy of people involed.
Consequently, it becomes increasingly important to preserve the privacy of
published data. An attacker is apt to identify an individual from the published
tables, with attacks through the record linkage, attribute linkage, table linkage or
probabilistic attack. Although algorithms based on generalization and suppression
have been proposed to protect the sensitive attributes and resist these multiple
types of attacks, they often suffer from large information loss by replacing specific
values with more general ones. Alternatively, anatomization and permutation
operations can de-link the relation between attributes without modifying them. In
this paper, we propose a scheme Sensitive Label Privacy Preservation with
Anatomization (SLPPA) to protect the privacy of published data. SLPPA includes
two procedures, table division and group division. During the table division, we
adopt entropy and mean-square contingency coefficient to partition attributes into
separate tables to inject uncertainty for reconstructing the original table. During the
group division, all the individuals in the original table are partitioned into non-
overlapping groups so that the published data satisfies the pre-defined privacy
requirements of our (α, β, γ, δ) model. Two comprehensive sets of real-world
relationship data are applied to evaluate the performance of our anonymization

approach. Simulations and privacy analysis show our scheme possesses better
privacy while ensuring higher utility.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
 System : Pentium Dual Core.
 Hard Disk : 120 GB.
 Monitor : 15’’ LED
 Input Devices : Keyboard, Mouse
 Ram : 1 GB
SOFTWARE REQUIREMENTS:
 Operating system : Windows 7.
 Coding Language : JAVA.
 Tool : Netbeans 7.2.1
 Database : MYSQL
REFERENCE:
Lin Yao ; Zhenyu Chen ; Xin Wang ; Dong Liu ; Guowei Wu, “Sensitive Label
Privacy Preservation with Anatomization for Data Publishing”, IEEE Transactions
on Dependable and Secure Computing, 2019.

Sensitive Label Privacy Preservation with Anatomization for Data Publishing

Data integrity refers to maintaining accurate and consistent data throughout its lifecycle. It is important for information security as it prevents unauthorized changes to data from storage, retrieval, processing or malicious intent. Entity integrity and referential integrity help ensure data integrity. Entity integrity uses primary keys to uniquely identify rows and prevent duplicates. Referential integrity uses foreign keys to link data across tables and ensure changes are propagated properly.

Computation systems for protecting delimited data

G Prachi

Computational systems for protecting delimited data include: - MinGen, which uses generalization and suppression techniques - Datafly System, which generalizes values based on the recipient's profile and ensures k tuples match to maintain anonymity - μ-Argus System, which enforces a k requirement on attributes and suppresses outlier values at the cell level - k-Similar Algorithm, which avoids re-identification by ensuring at least k tuples match on the quasi-identifier - Scrub System, which locates and suppresses or replaces identifying information in text to protect identities while maintaining integrity.

A random decision tree frameworkfor privacy preserving data mining

Venkat Projects

This document summarizes a framework for privacy-preserving data mining using a random decision tree algorithm. Multiple parties like banks, insurance companies, and credit card companies share data but need to keep certain attributes private. The random decision tree algorithm partitions data based on each party's needs, encrypts the data using homomorphic encryption, builds a decision tree model on the encrypted data, and allows parties to classify new instances while preserving privacy. It compares the accuracy of random decision trees to traditional ID3 decision trees.

Data management

Madrasah Idrisiah

SharedPreferences stores private primitive data, is automatically persisted between sessions, and is often used for customizable app settings. SQLite databases store larger amounts of structured private data locally. Internal storage saves smaller private data sets while external storage saves larger non-private data. The document provides examples of using these storage options to save and retrieve data like preferences, images and database records in an Android app.

Data management

maamir farooq

JAVA 2013 IEEE NETWORKSECURITY PROJECT Utility privacy tradeoff in databases ...

IEEEGLOBALSOFTTECHNOLOGIES

Utility privacy tradeoff in databases an information-theoretic approach

IEEEFINALYEARPROJECTS

The document summarizes a research paper published in the IEEE Transactions on Knowledge and Data Engineering in 2012 that introduces a new technique called "slicing" for privacy-preserving data publishing. Slicing partitions data both horizontally into buckets of tuples and vertically into columns of correlated attributes. Within each bucket and column, attribute values are randomly permuted. Slicing aims to better preserve data utility compared to generalization while also addressing privacy issues like membership disclosure that bucketization cannot prevent. The paper presents the slicing technique, compares it to generalization and bucketization, develops an algorithm for efficient l-diverse slicing, and evaluates slicing through experiments.

78201919

IJRAT

78201919

IJRAT

This document proposes a refinement of the slicing anonymization technique for privacy-preserving data mining. Slicing anonymization has been shown to effectively preserve data quality while achieving high data privacy. The proposed refinement aims to achieve even higher data utility and more secure data publishing through probabilistic non-homogeneous suppression and consideration of attribute correlations. The results of applying the technique to election data are analyzed using standard classification metrics to validate that the refined approach maintains high data quality and strong privacy preservation.

Data attribute security and privacy in Collaborative distributed database Pub...

International Journal of Engineering Inventions www.ijeijournal.com

In this era, there are need to secure data in distributed database system. For collaborative data publishing some anonymization techniques are available such as generalization and bucketization. We consider the attack can call as “insider attack” by colluding data providers who may use their own records to infer others records. To protect our database from these types of attacks we used slicing technique for anonymization, as above techniques are not suitable for high dimensional data. It cause loss of data and also they need clear separation of quasi identifier and sensitive database. We consider this threat and make several contributions. First, we introduce a notion of data privacy and used slicing technique which shows that anonymized data satisfies privacy and security of data which classifies data vertically and horizontally. Second, we present verification algorithms which prove the security against number of providers of data and insure high utility and data privacy of anonymized data with efficiency. For experimental result we use the hospital patient datasets and suggest that our slicing approach achieves better or comparable utility and efficiency than baseline algorithms while satisfying data security. Our experiment successfully demonstrates the difference between computation time of encryption algorithm which is used to secure data and our system.

Protecting Attribute Disclosure for High Dimensionality and Preserving Publis...

IOSR Journals

This document summarizes a research paper on a novel technique called "slicing" for privacy-preserving publication of microdata. Slicing partitions data both horizontally into buckets and vertically into correlated attribute columns. This preserves more utility than generalization while preventing attribute and membership disclosure better than bucketization. Experiments on census data show slicing outperforms other methods in preserving utility and privacy for high-dimensional and sensitive attribute workloads. Slicing groups correlated attributes to maintain useful correlations and breaks links between uncorrelated attributes that pose privacy risks.

Proximity aware local-recoding anonymization with map reduce for scalable big...

Nexgen Technology

TO GET THIS PROJECT COMPLETE SOURCE CODE PLEASE CALL BEOLOW CONTACT DETAILS MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM ,EMAIL:Praveen@nexgenproject.com NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.

An New Attractive Mage Technique Using L-Diversity

mlaij

Data that is published or shared between organizations contain private information about an individual. The concept of Privacy Preservation aims to preserve this sensitive information from various privacy threats that violate the privacy of an individual. Analysis of this private information could reveal information that can be used for malicious purposes by the attackers. Anonymization is a privacy preservation approach suitable for mixed data that contains both numerical and categorical attributes. In this paper a novel method called Micro-aggregation Generalization (MAGE) is used for anonymization of microdata that can retain more semantics of the original data. Here the Micro-aggregation is applied over the numerical data and Generalization is applied over the categorical data. Even though the MAGE approach preserves privacy it fails to address the homogeneity and background knowledge attacks. Later the l-diversity approach is applied to deal with homogeneity attack. In l-diversity, the anonymized records are reordered to satisfy a new privacy principle that removes homogeneity of sensitive information. The result shows that the MAGE approach suffers from homogeneity attack and applying l-diversity over MAGE prevents homogeneity attack and also provides better privacy and data utility.

Query Processing with k-Anonymity

Waqas Tariq

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.

Isaca journal - bridging the gap between access and security in big data...

Ulf Mattsson

Organizations are failing to truly secure sensitive data in big data environments due to prioritizing data access over security. Traditional security methods obstruct access. Tokenization bridges this gap by replacing sensitive data with randomized tokens, securing data while still enabling analytics. A proper data security methodology includes classifying sensitive data, discovering its locations, applying the best security method like tokenization, enforcing policy, and monitoring access. This balances privacy, usability, and compliance.

Ppback propagation-bansal-zhong-2010

girivaishali

This document proposes a privacy-preserving algorithm for backpropagation neural network learning when the training data is arbitrarily partitioned between two parties. Existing approaches only address vertically or horizontally partitioned data. The proposed algorithm keeps each party's data private during training, revealing only the final learned weights. It aims to be efficient in computation and communication overhead while providing strong privacy guarantees. The algorithm uses secure scalar product and techniques from previous work on vertically partitioned data to perform training without either party learning about the other's data.

A Study on Genetic-Fuzzy Based Automatic Intrusion Detection on Network Datasets

Drjabez

1. The document proposes a genetic-fuzzy based method for automatic intrusion detection using network datasets. It combines fuzzy set theory with genetic algorithms to extract rules for both discrete and continuous attributes to detect normal and intrusion patterns. 2. The method was tested on KDD99 Cup and DARPA98 network intrusion detection datasets and showed high detection rates with low false alarm rates for both misuse detection and anomaly detection. 3. By extracting many rules to represent normal network behavior patterns, the proposed genetic-fuzzy approach can detect new or unknown intrusions based on anomalies without requiring prior domain expertise on intrusion patterns.

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Huge volume of data from domain specific applications such as medical, financial, library, telephone, shopping records and individual are regularly generated. Sharing of these data is proved to be beneficial for data mining application. On one hand such data is an important asset to business decision making by analyzing it. On the other hand data privacy concerns may prevent data owners from sharing information for data analysis. In order to share data while preserving privacy, data owner must come up with a solution which achieves the dual goal of privacy preservation as well as an accuracy of data mining task – clustering and classification. An efficient and effective approach has been proposed that aims to protect privacy of sensitive information and obtaining data clustering with minimum information loss

Protection models

G Prachi

This document discusses models for protecting data privacy, including null-map, k-map, and wrong-map protection models. It describes techniques for anonymizing data such as generalization, suppression, and perturbation methods like adding noise or swapping values. The goal of these statistical disclosure control techniques is to modify data such that private information cannot be linked to specific individuals while still allowing useful analysis of the de-identified data.

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Enabling Use of Dynamic Anonymization for Enhanced Security in Cloud

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This document summarizes various techniques for anonymizing data to protect privacy and security when data is stored in the cloud. It discusses how anonymization removes identifying attributes from data to prevent individuals from being identified. The document reviews existing anonymization models like k-anonymity, l-diversity and t-closeness. It then describes different anonymization techniques like hashing, hiding, permutation, shifting, truncation, prefix-preserving and enumeration that were implemented to anonymize data fields. The goal is to anonymize data in a way that balances privacy, security, and the ability to still use the data for appropriate purposes.

A Rule based Slicing Approach to Achieve Data Publishing and Privacy

ijsrd.com

several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving micro data publishing. Recent work has shown that generalization loses considerable amount of information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. The existing system proposed slicing concept to overcome the tuple based partition this has been done to overcome the previous generalization and bucketization. In this paper, present a novel technique called rule based slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than generalization and can be used for membership disclosure protection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an efficient algorithm for computing the sliced data that obey the l-diversity requirement. The workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. The experiments also demonstrate that slicing can be used to prevent membership disclosure

Implementation of De-Duplication Algorithm

IRJET Journal

The document describes an implementation of a data de-duplication algorithm using convergent encryption. It discusses how data de-duplication works to reduce storage usage by identifying and removing duplicate copies of data. Convergent encryption is used, which generates the same encrypted form of a file from the original file's hash, allowing duplicate encrypted files to be de-duplicated while preserving privacy. The algorithm divides files into blocks, generates hashes for each block, and encrypts the file blocks using the hashes as keys. When a file is uploaded, its hash is checked against existing hashes to identify duplicates, with duplicates replaced by pointers to the stored copy. This allows efficient de-duplication while encrypting data for privacy and security when stored

IRJET - Identifying Information Relocate with Reliable Estimation and Sec...

IRJET Journal

This document summarizes a research paper that proposes a method for ensuring data integrity and privacy when data is stored on cloud computing systems. The method uses blockchain techniques and distributed verification to provide redundancy and guarantee data reliability. It allows both data owners and public verifiers to check data integrity without downloading the entire dataset. The technique utilizes homomorphic tokens and ring signatures to enable auditing while preventing privacy leaks about user identities or data contents. Prior works on remote data integrity lacked either public auditing or support for dynamic data operations, but the proposed method achieves both.

winbis1005

vamshi batchu

This paper proposes a classification-based approach for suppressing data to prevent sensitive information from being inferred. It uses decision tree algorithms to classify data elements based on attributes and considers suppressing data elements to secure the data. The paper aims to enhance data classification and generalization. It shows how data can be secured using "generalization" while maintaining usefulness for data mining tasks. The proposed system focuses on data generalization concepts to hide detailed information for privacy while allowing standard data mining techniques to still discover patterns. It evaluates suppressing multiple confidential values and developing a technique independent of individual classification methods based on information theory.

JAVA 2013 IEEE PARALLELDISTRIBUTION PROJECT A privacy leakage upper bound con...

IEEEGLOBALSOFTTECHNOLOGIES

The document proposes an approach to identify which intermediate datasets generated during cloud-based data processing need to be encrypted to preserve privacy, while avoiding encrypting all datasets which is inefficient and costly. It models the generation relationships between datasets and uses an upper-bound constraint on privacy leakage to determine which datasets exceed the threshold. This is formulated as an optimization problem to minimize privacy-preserving costs. Evaluation on real-world datasets shows the approach significantly reduces costs compared to fully encrypting all intermediate datasets.

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Sensitive Label Privacy Preservation with Anatomization for Data Publishing

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