Information security in big data privacy and data mining pdf

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information security in big data privacy and data mining pdf

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Data mining

Metrics details. Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results.

Big data analytics is the term used to describe the process of researching massive amounts of complex data in order to reveal hidden patterns or identify secret correlations. However, there is an obvious contradiction between the security and privacy of big data and the widespread use of big data. This paper focuses on privacy and security concerns in big data, differentiates between privacy and security and privacy requirements in big data.

This paper covers uses of privacy by taking existing methods such as HybrEx, k-anonymity, T-closeness and L-diversity and its implementation in business.

There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages for example, data generation, data storage, and data processing of a big data life cycle. The goal of this paper is to provide a major review of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms.

This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, identity based anonymization, differential privacy, privacy-preserving big data publishing and fast anonymization of big data streams. This paper refer privacy and security aspects healthcare in big data. Comparative study between various recent techniques of big data privacy is also done as well.

Big data [ 1 , 2 ] specifically refers to data sets that are so large or complex that traditional data processing applications are not sufficient. Due to recent technological development, the amount of data generated by internet, social networking sites, sensor networks, healthcare applications, and many other companies, is drastically increasing day by day.

All the enormous measure of data produced from various sources in multiple formats with very high speed [ 3 ] is referred as big data. Later studies pointed out that the definition of 3Vs is insufficient to explain the big data we face now.

Thus, veracity, validity, value, variability, venue, vocabulary, and vagueness were added to make some complement explanation of big data [ 6 ]. A common theme of big data is that the data are diverse, i. This differing qualities of data is signified by variety.

In order to ensure big data privacy, various mechanisms have been developed in recent years. These mechanisms can be grouped based on the stages of big data life cycle [ 7 ] Fig. In data generation phase, for the protection of privacy, access restriction as well as falsifying data techniques are used.

The approaches to privacy protection in data storage phase are chiefly based on encryption procedures. In addition, to protect the sensitive information, hybrid clouds are utilized where sensitive data are stored in private cloud.

In PPDP, anonymization techniques such as generalization and suppression are utilized to protect the privacy of data. These mechanisms can be further divided into clustering, classification and association rule mining based techniques. While clustering and classification split the input data into various groups, association rule mining based techniques find the useful relationships and trends in the input data [ 8 ]. To handle diverse measurements of big data in terms of volume, velocity, and variety, there is need to design efficient and effective frameworks to process expansive measure of data arriving at very high speed from various sources.

Big data needs to experience multiple phases during its life cycle. Big data life cycle stages of big data life cycle, i. As of , 2.

Lightweight incremental algorithms should be considered that are capable of achieving robustness, high accuracy and minimum pre-processing latency.

Like, in case of mining, lightweight feature selection method by using Swarm Search and Accelerated PSO can be used in place of the traditional classification methods [ 10 ].

Further ahead, Internet of Things IoT would lead to connection of all of the things that people care about in the world due to which much more data would be produced than nowadays [ 11 ].

Indeed, IoT is one of the major driving forces for big data analytics [ 9 ]. Smart energy big data analytics is also a very complex and challenging topic that share many common issues with the generic big data analytics. Smart energy big data involve extensively with physical processes where data intelligence can have a huge impact to the safe operation of the systems in real-time [ 12 ].

This can also be useful for marketing and other commercial companies to grow their business. As the database contains the personal information, it is vulnerable to provide the direct access to researchers and analysts.

Since in this case, the privacy of individuals is leaked, it can cause threat and it is also illegal. The paper is based on research not ranging to a specific timeline. As the references suggest, research papers range from as old as to papers published in Also, the number of papers that were retrieved from the keyword-based search can be verified from the presence of references based on the keywords. Privacy and security in terms of big data is an important issue. Big data security model is not suggested in the event of complex applications due to which it gets disabled by default.

However, in its absence, data can always be compromised easily. As such, this section focuses on the privacy and security issues. Privacy Information privacy is the privilege to have some control over how the personal information is collected and used. Information privacy is the capacity of an individual or group to stop information about themselves from becoming known to people other than those they give the information to.

One serious user privacy issue is the identification of personal information during transmission over the Internet [ 13 ]. Security Security is the practice of defending information and information assets through the use of technology, processes and training from:-Unauthorized access, Disclosure, Disruption, Modification, Inspection, Recording, and Destruction.

Privacy vs. Security concentrates more on protecting data from malicious attacks and the misuse of stolen data for profit [ 14 ]. Big data analytics draw in various organizations; a hefty portion of them decide not to utilize these services because of the absence of standard security and privacy protection tools.

These sections analyse possible strategies to upgrade big data platforms with the help of privacy protection capabilities. The foundations and development strategies of a framework that supports:.

The specification of privacy policies managing the access to data stored into target big data platforms,. The integration of the generated monitors into the target analytics platforms. Enforcement techniques proposed for traditional DBMSs appear inadequate for the big data context due to the strict execution necessities needed to handle large data volumes, the heterogeneity of the data, and the speed at which data must be analysed.

Businesses and government agencies are generating and continuously collecting large amounts of data. The current increased focus on substantial sums of data will undoubtedly create opportunities and avenues to understand the processing of such data over numerous varying domains. Ensures conformance to privacy terms and regulations are constrained in current big data analytics and mining practices.

To address these challenges, identify a need for new contributions in the areas of formal methods and testing procedures. Big data architecture and testing area new paradigms for privacy conformance testing to the four areas of the ETL Extract, Transform, and Load processes are shown here. At this step, the privacy specifications characterize the sensitive pieces of data that can uniquely identify a user or an entity. Privacy terms can likewise indicate which pieces of data can be stored and for how long.

At this step, schema restrictions can take place as well. Privacy terms can tell the minimum number of returned records required to cover individual values, in addition to constraints on data sharing between various processes. ETL process validation Similar to step 2 , warehousing rationale should be confirmed at this step for compliance with privacy terms. Some data values may be aggregated anonymously or excluded in the warehouse if that indicates high probability of identifying individuals.

Reports testing reports are another form of questions, conceivably with higher visibility and wider audience. Data generation can be classified into active data generation and passive data generation. Minimization of the risk of privacy violation amid data generation by either restricting the access or by falsifying data. Access restriction If the data owner thinks that the data may uncover sensitive information which is not supposed to be shared, it refuse to provide such data.

If the data owner is giving the data passively, a few measures could be taken to ensure privacy, such as anti-tracking extensions, advertisement or script blockers and encryption tools. Falsifying data In some circumstances, it is unrealistic to counteract access of sensitive data.

In that case, data can be distorted using certain tools prior to the data gotten by some third party. If the data are distorted, the true information cannot be easily revealed. The following techniques are utilized by the data owner to falsify the data:. A tool Socketpuppet is utilized to hide online identity of individual by deception. By utilizing multiple Socketpuppets, the data belonging to one specific individual will be regarded as having a place with various people.

In that way the data collector will not have enough knowledge to relate different socketpuppets to one individual. This is especially useful when the data owner needs to give the credit card details amid online shopping. Storing high volume data is not a major challenge due to the advancement in data storage technologies, for example, the boom in cloud computing [ 18 ].

In distributed environment, an application may need several datasets from various data centres and therefore confront the challenge of privacy protection. The conventional security mechanisms to protect data can be divided into four categories. They are file level data security schemes, database level data security schemes, media level security schemes and application level encryption schemes [ 20 ]. It should have the ability to be configured dynamically to accommodate various applications.

One promising technology to address these requirements is storage virtualization, empowered by the emerging cloud computing paradigm [ 21 ]. Storage virtualization is process in which numerous network storage devices are combined into what gives off an impression of being a single storage device.

SecCloud is one of the models for data security in the cloud that jointly considers both of data storage security and computation auditing security in the cloud [ 22 ]. Therefore, there is a limited discussion in case of privacy of data when stored on cloud. When data are stored on cloud, data security predominantly has three dimensions, confidentiality, integrity and availability [ 23 ].

The first two are directly related to privacy of the data i. Availability of information refers to ensuring that authorized parties are able to access the information when needed. A basic requirement for big data storage system is to protect the privacy of an individual. There are some existing mechanisms to fulfil that requirement. For example, a sender can encrypt his data using pubic key encryption PKE in a manner that only the valid recipient can decrypt the data.

The approaches to safeguard the privacy of the user when data are stored on the cloud are as follows [ 7 ]:.

Data mining

Still, several important issues need to be addressed to capture the full potential of big data. As shown by the recent Cambridge Analytica scandal Cadwalladr and Graham-Harrison, where millions of users profile information were misused, security and privacy issues become a critical concern. As big data becomes the new oil for the digital economy, realizing the benefits that big data can bring requires considering many different security and privacy issues. This in return implies that the entire big data pipeline needs to be revisited with security and privacy in mind. For example, while the big data is stored and recorded, appropriate privacy-aware access control policies need to be enforced so that the big data is only used for legitimate purposes.


PDF | The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive.


Privacy Issues in Big Data Mining Infrastructure, Platforms, and Applications

Metrics details. Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results. Big data analytics is the term used to describe the process of researching massive amounts of complex data in order to reveal hidden patterns or identify secret correlations. However, there is an obvious contradiction between the security and privacy of big data and the widespread use of big data.

Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Information Security in Big Data: Privacy and Data Mining Abstract: The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information.

Big data security challenges and strategies

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