83,218 research outputs found
Distributed Private Online Learning for Social Big Data Computing over Data Center Networks
With the rapid growth of Internet technologies, cloud computing and social
networks have become ubiquitous. An increasing number of people participate in
social networks and massive online social data are obtained. In order to
exploit knowledge from copious amounts of data obtained and predict social
behavior of users, we urge to realize data mining in social networks. Almost
all online websites use cloud services to effectively process the large scale
of social data, which are gathered from distributed data centers. These data
are so large-scale, high-dimension and widely distributed that we propose a
distributed sparse online algorithm to handle them. Additionally,
privacy-protection is an important point in social networks. We should not
compromise the privacy of individuals in networks, while these social data are
being learned for data mining. Thus we also consider the privacy problem in
this article. Our simulations shows that the appropriate sparsity of data would
enhance the performance of our algorithm and the privacy-preserving method does
not significantly hurt the performance of the proposed algorithm.Comment: ICC201
Review on Present State-of-the-Art of Secure and Privacy Preserving Data Mining Techniques
As people of every walk of life are using Internet for various purposes there is growing evidence of proliferation of sensitive information. Security and privacy of data became an important concern. For this reason privacy preserving data mining (PPDM) has been an active research area. PPDM is a process discovering knowledge from voluminous data while protecting sensitive information. In this paper we explore the present state-of-the-art of secure and privacy preserving data mining algorithms or techniques which will help in real world usage of enterprise applications. The techniques discussed include randomized method, k-Anonymity, l-Diversity, t-Closeness, m-Privacy and other PPDM approaches. This paper also focuses on SQL injection attacks and prevention measures. The paper provides research insights into the areas of secure and privacy preserving data mining techniques or algorithms besides presenting gaps in the research that can be used to plan future research
Privacy-Preserving Clustering of Data Streams
[[abstract]]As most previous studies on privacy-preserving data mining placed specific importance on the security of massive amounts of data from a static database, consequently data undergoing privacy-preservation often leads to a decline in the accuracy of mining results. Furthermore, following by the rapid advancement of Internet and telecommunication technology, subsequently data types have transformed from traditional static data into data streams with consecutive, rapid, temporal, and unpredictable properties. Due to the increase of such data types, traditional privacy-preserving data mining algorithms requiring complex calculation are no longer applicable.
As a result, this paper has proposed a method of Privacy-Preserving Clustering of Data Streams (PPCDS) to improve data stream mining procedures while concurrently preserving privacy with a high degree of mining accuracy. PPCDS is mainly composed of two phases: Rotation-Based Perturbation and cluster mining. In the phase of data rotating perturbation phase, a rotation transformation matrix is applied to rapidly perturb the data streams in order to preserve data privacy. In the cluster mining phase, perturbed data will first establish a micro-cluster through optimization of cluster centers, then applying statistical calculation to update a micro-cluster, as well as using geometric time frame to allocate and store a micro-cluster, and finally output mining result through a macro-cluster generation. Two simple data structure are added in the macro-cluster generation process to avoid recalculating the distance between the macro-point and the cluster center in the generation process. This process reduces the repeated calculation time in order to enhance mining efficiency without losing mining accuracy.[[incitationindex]]EI[[booktype]]ç´™
Big Data Privacy Context: Literature Effects On Secure Informational Assets
This article's objective is the identification of research opportunities in
the current big data privacy domain, evaluating literature effects on secure
informational assets. Until now, no study has analyzed such relation. Its
results can foster science, technologies and businesses. To achieve these
objectives, a big data privacy Systematic Literature Review (SLR) is performed
on the main scientific peer reviewed journals in Scopus database. Bibliometrics
and text mining analysis complement the SLR. This study provides support to big
data privacy researchers on: most and least researched themes, research
novelty, most cited works and authors, themes evolution through time and many
others. In addition, TOPSIS and VIKOR ranks were developed to evaluate
literature effects versus informational assets indicators. Secure Internet
Servers (SIS) was chosen as decision criteria. Results show that big data
privacy literature is strongly focused on computational aspects. However,
individuals, societies, organizations and governments face a technological
change that has just started to be investigated, with growing concerns on law
and regulation aspects. TOPSIS and VIKOR Ranks differed in several positions
and the only consistent country between literature and SIS adoption is the
United States. Countries in the lowest ranking positions represent future
research opportunities.Comment: 21 pages, 9 figure
Privacy Preservation using T-Closeness with Numerical Attributes
Data mining is a process that is used to retrieve the knowledgeable data from the large dataset. Information imparting around two associations will be basic done a large number requisition zones. As people are uploading their personal data over the internet, however the data collection and data distribution may lead to disclosure of their privacy. So, preserving the privacy of the sensitive data is the challenging task in data mining. Many organizations or hospitals are analyzing the medical data to predict the disease or symptoms of disease. So, before sharing data to other organization need to protect the patient personal data and for that need privacy preservation. In the recent year�s privacy preserving data mining has being received a large amount of attention in the research area. To achieve the expected goal various methods have been proposed. In this paper, to achieve this goal a pre-processing technique i.e. k-means clustering along with anonymization technique i.e. k-anonymization and t-closeness and done analysis which techniques achieves more information gain
Recommended from our members
The Voyeur among Us: Navigating Around the Global Spyware Epidemic
Spyware poses a serious threat of privacy infringement to unassuming internet users across the globe. Existing European legislation attempts to protect end-users from unethical review and use of their personal data. Outlawing spyware technology and strengthening the legal consent requirement for data- mining may offer end users additional assurances that their privacy rights are upheld, as well as more tangible shelter from the existing spyware epidemic. These proposed solutions, however, will only create successful safe havens for internet users by obtaining international buy-in
Comprehensive survey on big data privacy protection
In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of confidential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a specific approach to enable the development of a good data mining model on modified data, thereby meeting a specified privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals’ sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classified using various approaches for data modification. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM.Published versio
Internet Giants as Quasi-Governmental Actors and the Limits of Contractual Consent
Although the government’s data-mining program relied heavily on information and technology that the government received from private companies, relatively little of the public outrage generated by Edward Snowden’s revelations was directed at those private companies. We argue that the mystique of the Internet giants and the myth of contractual consent combine to mute criticisms that otherwise might be directed at the real data-mining masterminds. As a result, consumers are deemed to have consented to the use of their private information in ways that they would not agree to had they known the purposes to which their information would be put and the entities – including the federal government – with whom their information would be shared. We also call into question the distinction between governmental actors and private actors in this realm, as the Internet giants increasingly exploit contractual mechanisms to operate with quasi-governmental powers in their relations with consumers. As regulators and policymakers focus on how to better protect consumer data, we propose that solutions that rely upon consumer permission adopt a more exacting and limited concept of the consent required before private entities may collect or make use of consumer’s information where such uses touch upon privacy interests
- …