Personal Data Privacy and Protection in the Meeting, Incentive, Convention, and Exhibition (MICE) Industry

Personal Data Privacy and Protection in the Meeting, Incentive, Convention, and Exhibition (MICE) Industry

M. Fevzi Esen (Istanbul Medeniyet University, Turkey) and Eda Kocabas (Istanbul Medeniyet University, Turkey)
Copyright: © 2019 |Pages: 27
DOI: 10.4018/978-1-5225-7432-3.ch023
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With the new developments in information technologies, personal and business data have become easily accessible through different channels. The huge amounts of personal data across global networks and databases have provided crucial benefits in a scientific manner and many business opportunities, also in the meeting, incentive, convention, and exhibition (MICE) industry. In this chapter, the authors focus on the analysis of MICE industry with regards to the new regulation (GDPR) of personal data protection of all EU citizens and how the industry professionals can adapt their way of business in light of this new regulation. The authors conducted an online interview with five different meetings industry professionals to have more insight about the data produced with its content and new regulations applied to the industry. The importance of personal data privacy and protection is discussed, and the most suitable anonymization techniques for personal data privacy are proposed.
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New technologies changed open or private data in personal or business environments. The big amounts of gigabytes and terabytes of disseminated personal or business data across global networks and databases have brought significant advantages for scientific understanding and business opportunities. Since the individuals are more involved in information and communication practices, they produce various forms of distributed and persistent data in different dimensions and structures. Moreover, data about the objects of physical life is also migrated to virtual environments and this data is combined and aggregated with online data (Brown & Marsden, 2013). This reality creates the phases of data lifecycle including the processes of collecting, saving, storing, reporting, sharing, analyzing and interpreting data.

Digitalization has allowed all individuals to contribute to worldwide big data mass. An estimated of 1.8 ZB data is produced in 2011 and this number is expected to reach approximately 90 ZB by 2020 (Jeon, 2012). It is expected that personal data can generate an economic benefit of $387 billion annually by 2020 (Liem & Petropoulos, 2016). Consumers create enormous data stream during their communication, searches, transactions and travels through connected devices. They leave a digital footprint behind that contains their personal information. Seventy five percent of digitally created data is composed of personal data and almost seventy percent of consumers take action to protect their privacies across all industries (Cooper et al., 2016). This reality generates personal data privacy concerns and the need of safeguarding data. Only in the U.S. A., more than $2 billion is spent to assure personal data privacy and protection (Khatibloo et. al., 2016). Despite of increasing volume of data creation every second, traditional systems have failed to resolve issues related to privacy while enabling data to be used effectively (Izu et al., 2014).

The increase in the amount of electronic data makes information systems necessary to evaluate various data types that are not held in relational databases but which contain hierarchical and semantic labels. While structured data following a data model and organized as relational tables are stored in analytically configured databases, unstructured data have a complexity in a dynamic range of its sources that may come from social media shares, mobile data, e-mails and text messages, sensor data and semantic web which are kept in specialized database systems. In order to discover knowledge from structured and unstructured data, the technologies have been developed that can systematically transform data and manage it in appropriate databases and integrate it with other data sources.

Data sharing among individuals, institutions and countries arises the problem of conflict of interest between data owners and holders against data owners. The issue of personal data privacy protection has been addressed in various national and international regulations within this context.

Personal data, as an asset, with its growing financial value has a crucial role in modern economies. Companies invest in data technologies to transform their business processes through digital intelligence for gaining a competitive advantage. According to International Association of Privacy Professionals and EY (IAPP-EY) Report on data privacy governance of 2017, companies spend a mean of $5 million in adaptation of their products and services for personal data privacy protection. Estimated data privacy spent per employee rose from $124 to $147 in 2017 (IAPP-EY, 2017). Moreover, more than three quarters of consumers believe that most companies are not able to handle personal data privacy protection and its related issues (PwC, 2017).

Key Terms in this Chapter

Big Data: In MICE industry, it is a common tool in following and tracking some useful information in regards to reaching the targeted meetings’ location history, subject details, national representatives, local ambassadors, hosting organizations and contact details of sponsors that supported those meetings, events or exhibitions.

MICE Industry: MICE is an industry that refers to meetings, incentives, conventions, and exhibitions.

Personal Data: It is the data that needs to be protected if the subject person does not prefer to share it with other parties.

Data Privacy: The term refers to the confidentiality of information that one has and other parties are not allowed to share it without a consent of the data owner. Privacy is a measure of control for individuals about their personal information.

Noise Adding: This is applied to numerical values and the original values are changed by positive or negative deterioration with adding or multiplying a random value at certain ratios to quantitative attributes that are required to be protected.

Attribute and Record Suppression: In attribute suppression, sensitive attributes are removed as a single cell, tuple or entire column from the dataset. Instead of the entire column (variable), only specific records are removed from the dataset in record suppression.

Data Protection and Anonymization: Data protection is a task of safeguarding personal or sensitive data which are complex and widely distributed. Data anonymization is a process which generates solutions for threats and privacy problems that are related to personal data.

Data Swapping and Micro-Aggregation: Data swapping refers to permutation or shuffling that rearranges data by exchanging the values. Micro-aggregation is a perturbative technique which reduces the utility of data by creating new singular combinations in the dataset.

Global Recoding: In global recording, discrete or continuous numerical variables can be aggregated into a predefined class.

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