Personal Privacy Protection in Time of Big Data

Sokolova, Marina; Matwin, Stan

doi:10.1007/978-3-319-18781-5_18

Cited by 18 publications

(9 citation statements)

References 24 publications

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“…The cutting-edge nature of Big Data and analytics has driven wide-scale uptake of related technologies that have significantly outpaced the regulatory frameworks, resulting in ad hoc approaches that differ from agency to agency (Rubinstein, 2013; Smith & O’Malley, 2017; Townsend, 2014). Furthermore, regulations that do exist generally extend from outmoded legislation that did not anticipate the rapid advancement and openness of digital technologies (Sokolova & Matwin, 2016). For example, the Canadian Personal Information Protection and Electronic Documents Act (PIPEDA) relies on a purpose-consent model that was not designed to account for the multiple modes of data collection undertaken in the current digital era.…”

Section: Introductionmentioning

confidence: 99%

Constructing a Public Narrative of Regulations for Big Data and Analytics: Results From a Community-Driven Discussion

Popham

Lavoie

Coomber

2018

Social Science Computer Review

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This article reports on community perspectives about the regulation of municipality-led Big Data initiatives developed through an exploratory, deliberative democracy-informed approach. While analytics hold great promise for policy design and service delivery improvements, their mythologized nature may elicit a blind faith in empirical outcomes, leading to misrepresentation or omission of marginalized populations. Scholars have begun pointing to public consultation as a means of avoiding these challenges, suggesting that a truly “smart city” should vet potential Big Data polices through the community in order to identify locally relevant concerns. The Big Data in Cities: Barriers and Benefits symposium, held in May of 2017, took a deliberative democracy approach designed to contribute toward a midsized southern Ontario city’s regulatory framework for data aggregation and mobilization. Approximately 100 self-selected participants (primarily public advocates) attended a 2-day symposium that featured a series of presentations designed to introduce critiques to and strategies for the implementation of Big Data initiatives. Participants also engaged in several facilitated roundtable discussions during the symposium, and their transcribed conversations served as the data for this study. Thematic analysis identified three recurrent concerns: publicly vetted data ethics, consultation and literacy practices, and regulatory frameworks. The public consultation process employed by this study produced results that reflect critiques raised in other academic papers.

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Section: Introductionmentioning

confidence: 99%

Constructing a Public Narrative of Regulations for Big Data and Analytics: Results From a Community-Driven Discussion

Popham

Lavoie

Coomber

2018

Social Science Computer Review

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“…Take the first 32 bits of the square root of the first 8 prime numbers (2,3,5,7,11,13,17,19) in the natural number to get 8 parameters.…”

Section: Store Data On-chain and Out-of-chainmentioning

confidence: 99%

“…Secondly, such information management systems as credit reporting systems and academic systems involve the interests of many people, so the risk of being tampered with is very high. The disadvantage of a centralized database is that if someone has the right to modify the information, any changes can be made to the information, although the modification log is saved, but it is still saved in the centralized database and can still be deleted and modified [5]. Therefore, the difficulty in personnel information management in the field of big data is how to ensure the security of information is not leaky, and cannot be tampered and traceable.…”

Section: Introductionmentioning

confidence: 99%

Design of personnel big data management system based on blockchain

Chen

Song

2019

Future Generation Computer Systems

166

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h i g h l i g h t s• A method for solving blockchain information redundancy is proposed.• Analyze the shortcomings of the blockchain and the reasons for it. • Feasibility of combining blockchain with information management system. • Developed a prototype system to validate our ideas. a b s t r a c tWith the continuous development of information technology, enterprises, universities and governments are constantly stepping up the construction of electronic personnel information management system. The information of hundreds of thousands or even millions of people's information are collected and stored into the system. So much information provides the cornerstone for the development of big data, if such data is tampered with or leaked, it will cause irreparable serious damage. However, in recent years, electronic archives have exposed a series of problems such as information leakage, information tampering, and information loss, which has made the reform of personnel information management more and more urgent. The unique characteristics of the blockchain, such as non-tampering and traceability make it have great application potential in personnel information management, and can effectively solve many problems of traditional file management. However, the blockchain is limited by its own shortcomings such as small storage space and slow synchronization time, and cannot be directly applied to the big data field. This paper proposes a personnel management system based on blockchain, we analyzed the defects of the blockchain and proposed an improved method, constructs a novel data storage model of on-chain and out-of-chain that can effectively solve the problem of data redundancy and insufficient storage space. Based on this, we developed a prototype system with query, add, modify, and track personnel information, verified the feasibility of applying blockchain to personnel information management, explore the possibility of combining blockchain with big data.

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“…If the big data storage system is compromised, it can be exceptionally destructive as individuals' personal information can be disclosed [19]. In distributed environment, an application may need several datasets from various data centres and therefore confront the challenge of privacy protection.…”

Section: Big Data Privacy In Data Storage Phasementioning

confidence: 99%