scholarly journals One Size Does Not Fit All: Trade-offs between Misuse Probability and Level of Sanitization for Big Data

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3606-3611

Big data privacy has assumed importance as the cloud computing became a phenomenal success in providing a remote platform for sharing computing resources without geographical and time restrictions. However, the privacy concerns on the big data being outsourced to public cloud storage are still exist. Different anonymity or sanitization techniques came into existence for protecting big data from privacy attacks. In our prior works, we have proposed a misusability probability based metric to know the probable percentage of misusability. We additionally planned a system that suggests level of sanitization before actually applying privacy protection to big data. It was based on misusability probability. In this paper, our focus is on further evaluation of our misuse probability based sanitization of big data approach by defining an algorithm which willanalyse the trade-offs between misuse probability and level of sanitization. It throws light into the proposed framework and misusability measure besides evaluation of the framework with an empirical study. Empirical study is made in public cloud environment with Amazon EC2 (compute engine), S3 (storage service) and EMR (MapReduce framework). The experimental results revealed the dynamics of the trade-offs between them. The insights help in making well informed decisions while sanitizing big data to ensure that it is protected without losing utility required.

2014 ◽  
Vol 12 (1) ◽  
pp. 77-79 ◽  
Author(s):  
David Eckhoff ◽  
Christoph Sommer

Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang

Since its introduction in the early 21st century, mobile social media have played an indispensable part in contemporary human experiences. The convergence of social networking and mobile technologies and services creates a fascinating circumstance because the pervasive nature of mobile social networking technologies has impacted on users' privacy. The chapter employed a mixed research method to collect and analyze mobile social media users' experiences and privacy concerns in the age of Big Data. A total of 57 participants were included in this study. Collected data was analyzed by examining mobile social media users' experiences and their concerns over privacy. Findings from this study showed the rising concerns over personal privacy as a result of convergence of mobile social media and Big Data practices by the advertising industry. Theoretical and practical implications were discussed.


2014 ◽  
Vol 644-650 ◽  
pp. 1911-1914
Author(s):  
Shao Min Zhang ◽  
Yu Fang Gan ◽  
Bao Yi Wang

Considering the confidentiality and integrity of big data in cloud storage, a MapReduce-based AES parallelization scheme is designed by using MapReduce framework of the open source Hadoop in this paper. The scheme takes full advantage of MapReduce and modern cryptography technologies to parallelize AES encryption and decryption process, in the way of data decomposition, which speeds up the efficiency in the implementation of encryption and decryption. Meanwhile, mix plaintext, separate storage and other technical means are taken into account in this scheme to ensure the confidentiality and security of the key and the ciphertext. By analyzing the performance, it is proved that the time consumption of new scheme is significantly reduced comparing with the traditional method.


2016 ◽  
pp. 1528-1548
Author(s):  
Kenneth C. C. Yang ◽  
Yowei Kang

Since its introduction in the early 21st century, mobile social media have played an indispensable part in contemporary human experiences. The convergence of social networking and mobile technologies and services creates a fascinating circumstance because the pervasive nature of mobile social networking technologies has impacted on users' privacy. The chapter employed a mixed research method to collect and analyze mobile social media users' experiences and privacy concerns in the age of Big Data. A total of 57 participants were included in this study. Collected data was analyzed by examining mobile social media users' experiences and their concerns over privacy. Findings from this study showed the rising concerns over personal privacy as a result of convergence of mobile social media and Big Data practices by the advertising industry. Theoretical and practical implications were discussed.


2016 ◽  
Vol 25 (2) ◽  
pp. 312-329 ◽  
Author(s):  
BONNIE KAPLAN

Abstract:Electronic health records, data sharing, big data, data mining, and secondary use are enabling exciting opportunities for improving health and healthcare while also exacerbating privacy concerns. Two court cases about selling prescription data, the Sorrell case in the U.S. and the Source case in the U.K., raise questions of what constitutes “privacy” and “public interest”; they present an opportunity for ethical analysis of data privacy, commodifying data for sale and ownership, combining public and private data, data for research, and transparency and consent. These interwoven issues involve discussion of big data benefits and harms and touch on common dualities of the individual versus the aggregate or the public interest, research (or, more broadly, innovation) versus privacy, individual versus institutional power, identification versus identity and authentication, and virtual versus real individuals and contextualized information. Transparency, flexibility, and accountability are needed for assessing appropriate, judicious, and ethical data uses and users, as some are more compatible with societal norms and values than others.


2021 ◽  
Vol 8 (4) ◽  
pp. 685-733
Author(s):  
Jennifer Zwagerman

Technology advancements make life, work, and play easier and more enjoyable in many ways. Technology issues are also the cause of many headaches and dreams of living out the copier destruction scene from the movie “Office Space.” Whether it be user error or technological error, one key technology issue on many minds right now is how all the data produced every second of every day, in hundreds of different ways, is used by those that collect it. How much data are we talking about here? In 2018, the tech company Domo estimated that by 2020 “1.7 MB of data will be created every second” for every single person on Earth. In 2019, Domo’s annual report noted that “Americans use 4,416,720 GB of internet data including 188,000,000 emails, 18,100,000 texts and 4,497,420 Google searches every single minute.” And this was before the pandemic of 2020, which saw reliance on remote technology and the internet skyrocket. It is not just social media and working from home that generates data—the “Internet of Things” (“IoT”) is expanding exponentially. From our homes (smart appliances and thermostats), to entertainment (smart speakers and tablets), to what we wear (smartwatches and fitness devices), we are producing data constantly. Over 30 billion devices currently make up the IoT, and that number will double by 2025. The IoT is roughly defined as “devices—from simple sensors to smartphones and wearables—connected together.” That connection allows the devices to “talk” to each other across networks that stretch across the world, sharing information that in turn can be analyzed (alone or combined with data from other users) in ways that may be beneficial to the user or the broader economy. The key word in that last sentence is “may.” When it comes to the data that individuals and businesses across the world produce every second of every day, some of it—perhaps most of it—could be used in ways that are not beneficial to the user or the entire economy. Some data types can be used to cause harm in obvious ways, such as personal identifying information in cases of identity theft. While some data types may seem innocuous or harmful when viewed on their own, when combined with other data from the same user or even other users, it can be used in a wide variety of ways. While I find it beneficial to know how many steps I take in a day or how much time I sleep at night, I am not the only individual or entity with access to that information. The company that owns the device I wear also takes that information and uses it in ways that are beyond my control. Why would a company do that? In many instances, “[t]he data generated by the Internet of Things provides businesses with a wealth of information that—when properly collected, stored, and processed—gives businesses a depth of insight into user behavior never before seen.” Data security and privacy in general are issues that all companies manage as they work to protect the data we provide. Some types of data receive heightened protections, as discussed below, because they are viewed as personal, as private, or as potentially dangerous since unauthorized access to them could cause harm to the user/owner. Some states and countries have taken a step further, focusing not on industry-related data that needs particular types of protection, but in-stead looking at an individual’s overall right to privacy, particularly on the internet. Those protections are summarized below. It makes sense, you might say, to worry about financial or healthcare data remaining private and to not want every website you have ever visited to keep a file of information on you. But why might we care about the use of data in agricultural operations? Depending on who you ask, the answer may be that agricultural data needs no more care or concern than any other type of business data. Some argue that the use of “Big Data” in agriculture provides opportunities for smaller operations and shareholders. These opportunities include increased power in a market driven for many years by the mantra “bigger is better” and increased production of food staples across the world—both in a more environmentally-friendly fashion. While the benefits of technology and Big Data in the agricultural sector unarguably exist, questions remain as to how to best manage data privacy concerns in an industry where there is little specific law or regulation tied to collection, use, and ownership of this valuable agricultural production data. In the following pages, this Article discusses what types of data are currently being gathered in the agricultural sector and how some of that data can and is being used. In addition, it focuses on unique considerations tied to the use of agricultural data and why privacy concerns continue to increase for many producers. As the Article looks at potential solutions to privacy concerns, it summarizes privacy-related legislation that currently exists and ends by looking at whether any of the current privacy-related laws might be used or adapted within the agricultural sector to address potential misuse of agricultural data.


Author(s):  
K. G. Srinivasa ◽  
Ganesh Hegde ◽  
G M Sideesh ◽  
Srinidhi Hiriyannaiah

With the advent of increasing breaches and of private data stored in a large number of public cloud storage providers by high level government organizations under the pretext of copyright infringements, national security etc, data privacy is gaining paramount importance. Cheap yet powerful system-on-chip computers like the Raspberry Pi and wide range of community supported software have made running one's own server without spending a lot, a reality. We explore open source cloud storage service alternatives and their feature parity, running one of these services to setup a personal cloud server to protect data privacy and security. We also take a look at the security issues, data availability, cost effectiveness and viability of deploying a personal cloud when compared to some popular public cloud storage providers.


Author(s):  
D. Radhika ◽  
D. Aruna Kumari

Leakage and misuse of sensitive data is a challenging problem to enterprises. It has become more serious problem with the advent of cloud and big data. The rationale behind this is the increase in outsourcing of data to public cloud and publishing data for wider visibility. Therefore Privacy Preserving Data Publishing (PPDP), Privacy Preserving Data Mining (PPDM) and Privacy Preserving Distributed Data Mining (PPDM) are crucial in the contemporary era. PPDP and PPDM can protect privacy at data and process levels respectively. Therefore, with big data privacy to data became indispensable due to the fact that data is stored and processed in semi-trusted environment. In this paper we proposed a comprehensive methodology for effective sanitization of data based on misusability measure for preserving privacy to get rid of data leakage and misuse. We followed a hybrid approach that caters to the needs of privacy preserving MapReduce programming. We proposed an algorithm known as Misusability Measure-Based Privacy serving Algorithm (MMPP) which considers level of misusability prior to choosing and application of appropriate sanitization on big data. Our empirical study with Amazon EC2 and EMR revealed that the proposed methodology is useful in realizing privacy preserving Map Reduce programming.


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