The development of the digital age has accelerated the dissemination and exchange of information, which has brought great convenience to society and promoted the development of social economy, culture, politics, education, and other aspects. However, at the same time, it also brings some drawbacks. According to Romansky and Noninska (2020), the technology of the digital age has the potential to violate some basic principles of information security and privacy by unregulated access to information and personal data stored in different nodes of the global network.
The “digital age” (information age) is simply an era in which large amounts of data and information are widely available to many people through computer technology. Things like “big data”, “Internet of Things”, “cloud”, “artificial intelligence” and “automation” are all products of this era (David, 2019). The concept of “privacy” is based on the idea that an individual should not be observed or disturbed by others (Warren, 1890, as cited in Marwick & Boyd, 2018). This blog mainly introduces the use of data in the digital age and the resulting privacy leaks.
First of all, this is an era that is inseparable from the Internet, and a large number of users are active on major platforms every day. According to the 2022 Global Digital Report, global Internet users will spend 12.5 trillion hours online in 2022, with 5.31 billion mobile users, 4.95 billion Internet users, and 4.62 billion social media users. All of these user groups are up nearly 17% from 2021. People are also more and more willing to participate in sharing and exchanging information on various platforms. The Internet has permeated almost every aspect of our lives. We can shop online and watch our favorite books and movies online. But when using these services provided by various network platforms or mobile phone software, do most of them need you to register user accounts?
When you register a user account, do some platforms or software require you to provide some personal information? After filling out this personal information, you still need to tick the terms of service and privacy policy to complete the registration successfully, right? When you consciously or unconsciously complete this series of registrations, you have begun to “voluntarily” continue to provide your data to these platforms.
A large number of people did not clearly understand the content of the privacy policy during the registration process. Some people didn’t even click on the privacy policy to read it, or only glanced at it after clicking on a lot of content. According to the data provided by Pew Research Center, only 9 percent of adults in the U.S. say they always read a company’s privacy policy before agreeing to terms and conditions, while another 13 percent say they do so often. Additionally, 38% of Americans say they sometimes read these policies. Still, others give up reading these policies altogether: more than one-third of adults (36%) say they have never read a privacy policy before agreeing to it. Users lack control over their personal data, and after user authorization, the platform will not notify users how their personal information and data will be used.
However, some people clearly understand that the platform website will obtain and use the user’s personal information under certain circumstances through the privacy policy. However, in order to complete the registration and use the services and information provided by the platform, they compromised and provided their personal information to these platforms.
The platform improves the dimension of data capture through the behavioral dynamics of these users on the platform. For example, the photos posted by users on Weibo, Facebook, Twitter, etc., users’ daily browsing records and search records, etc., will be stored as data records by the platform. In this digital age, the increasingly large network platforms and mobile user groups have also generated massive amounts of data. Platforms will also exchange and share these data with each other for their own better business development. This can be found very well in our daily life. When we often search for a product or a piece of news of interest on a platform, and then we open another software, there will be advertisements about this product or related pushes for news of interest. I believe that many of us are in this situation.
This kind of data sharing also reduces the corresponding costs of repeated work such as data collection for each platform, improves the efficiency of each company, and plays an important role in economic development. According to Flew (2019), digital platform companies have become increasingly central to the economy and society. In this digital age, data has also become an important factor of production in the social economy.
However, there is also the problem of privacy leakage in the process of data collection and sharing. According to the 2021 Verizon Data Breach Investigations Report. The survey showed that of the 79,635 incidents analyzed from 88 countries and regions around the world, only 29,207 met their quality standards and 5,258 were confirmed as data breaches. Some organizations will use the network to illegally collect user data to violate personal privacy, such as hacking and phishing. These violated users will also suffer different degrees of psychological damage and property losses.
Phishing is a general term for a type of fraudulent act of obtaining personal data. By impersonating a trusted entity, getting in touch with a consumer’s personal device or everyday service in order to defraud personal data. Common phishing methods include spam emails with links, pop-ups in your browser, or carefully crafted fake websites, often followed by financial theft or scams (Fruhlinger, 2020). According to the 2021 Verizon Data Breach Investigations Report, phishing remains one of the most common breaches and leveraging COVID-19 has increased its frequency to appear in 36% of breaches, up from 25% last year. What makes a fake website credible? Successful phishers must not only demonstrate a high-credibility online presence to their victims; They must create an impressive presence so that victims cannot recognize the security measures installed in the web browser (Dhamija, Tygar, & Hearst, 2006).
Not only do users lose their privacy because data is collected by fake websites or other illegal organizations, but users’ data is also leaked at the storage stage of some company platforms they trust. Some platforms are constantly at risk of being maliciously attacked. Taking Facebook as an example, according to a news report by CNBC, an attack in September 2018 put the accounts of 50 million users at risk, after which the company published a blog saying that only 30 million users were actually considered at risk. Then, in 2021, according to the BBC’s report, a hacking forum released data on 533 million people from 106 countries, including Facebook user IDs, email addresses, and phone numbers. Facebook then said the data was old from a 2019 leak and was already being processed. This is the largest leak in the company’s history.
Regarding Facebook, there is also a high-profile case, the “Facebook-Cambridge Analytica” data scandal that harmed millions of Facebook users. In March 2018, political consulting firm Cambridge Analytica successfully accessed the personal data of as many as 87 million Facebook users, which was sold to third parties, including Donald Trump’s 2016 US presidential campaign (Flew, 2019). According to a report by Global News, the Cambridge Analytica-developed app “This is your digital life” requires Facebook users to agree to complete an academic survey. With the user’s consent, the program collects all the information on the user’s social network, but the data is ultimately used for political purposes. The scandal led to the US Federal Trade Commission fining the company $5 billion, the world’s largest to date, and imposing tough new privacy regulations on the company. This incident has seriously damaged the privacy of users. It can also be seen that some platforms may use the data for other purposes or even resell it for profit after legally collecting and storing user data.
User data is continuously collected and accumulated by various platforms. In order to better match users with the products of each company’s platform or provide users with better functions and services, big data analysis has become a technology commonly used by various platforms. Big data analysis is where advanced analysis technology operates on big data, and its core lies in data mining algorithms (Russom, 2011). Big data analysis is a process in which various platforms conduct data mining to discover data characteristics for detailed research and summary, so as to extract useful information from a large amount of data and form conclusions. Purohit and Singh (2013) stated that data collection strategies aimed at obtaining answers from different sources will allow researchers to describe, compare, and relate one feature to another and demonstrate that certain features exist in certain categories. Platforms profile users through big data analysis, such as categorization and behavior preference prediction.
A small number of large corporations possess “structural power” to shape access to information in the digital realm. A handful of search engines and content platforms, whose algorithms determine what we can do, can track user behavior, control personal data, and prioritize or even block specific content. (Horten, 2016, as cited in Karppinen).
Big data analysis has also caused some problems. For example, when using Douyin (the Chinese version of Tiktok), the platform will predict your preferences based on the number and duration of different video types you watch, so as to recommend more relevant content to you, and you will find that most of the videos you swipe are the type you like. Some users do not like to disclose their preferences and interests to others, which is their personal privacy. However, the collection of big data can often analyze and predict his preferences, values, etc. through his related browsing, likes, consumption, and other behaviors on digital platforms, which also causes an invasion of personal privacy for these users.
Some platforms even use this technique to discriminate against users by changing the price of products that a particular user frequently consumes on the platform. In the past few years, many news reports in China that Ctrip (the Chinese travel app) uses big data to change the price to treat users differently. One of the news is about two Ctrip users booking the same hotel with the same room type but at different prices. Users who frequently subscribe to the hotel are more expensive than those who seldom or never subscribe to the hotel. This behavior does not only happen on this one platform, users have similar experiences on many other platforms. Platforms use big data analysis to obtain user preferences, which not only violates users’ privacy but also harms users’ interests.
In this digital age, the rapid development of network technology has indeed brought great convenience to people’s lives, and the analysis of big data has also made it possible to more effectively match the needs of users, thereby bringing users better services and experiences. But at the same time, with the development trend, various network platforms continue to promote users to share and exchange more information, and personal information is always at risk of being leaked in the flow. Everyone seems to be a transparent person in front of big data, and personal privacy is always threatened. Therefore, as companies continue to improve their network defense systems to protect users’ data, they also need to consciously and strictly abide by the privacy treaty and have followed the government’s policy on privacy protection. Of course, users also need to strengthen and pay attention to the protection of their own privacy in order to obtain a better life experience in this ever-evolving digital age.
References List:
Dhamija, R., Tygar, J.D., & Hearst, M. (2006). Why phishing works. In CHI06: CHI 2006 Conference on Human Factors in Computing Systems (pp. 581-590). New York.
Flew, T. (2019). Platforms on Trial. Intermedia, 46(2) 18-23.
Fruhlinger, J. (2020). What is phishing? How this cyber attack works and how to prevent it. https://www.csoonline.com/article/2117843/what-is-phishing-how-this-cyber-attack-works-and-how-to-prevent-it.html
Karppinen, K. (2017). Human rights and the digital. In H. Tumber & S. Waisbord (Eds.), The Routledge Companion to Media and Human Rights (pp. 95-103). London.
Marwick, A. E., & Boyd, D. (2018). Understanding Privacy at the Margins: Introduction. International Journal of Communication, 12, 1157-1165.
Romansky, R. P., & Noninska, I. S. (2020). Challenges of the digital age for privacy and personal data protection. Mathematical Biosciences and Engineering, 17(5), 5288-5303.
Russom, P. (2011). Big Data Analytics. TDWI Best Practices Report, TDWI Research, Fourth Quarter.
Thomas, D. (2019). What is the digital age? https://www.ventivtech.com/blog/what-is-the-digital-age
Purohit, B., & Singh, P. P. (2013). Data leakage analysis on cloud computing. International Journal of Engineering Research and Applications, 3(3), 1311-1316.