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Firms that provide personalised services to users based on how other users behave puts users' personal information at risk, even if hackers don't directly gain access to the database. Hackers might get access to an algorithms' output for real users, then reverse-engineer the information to gain insight into a person's characteristics.
One strategy to combat this is to add some noise to an algorithm's output.
Research showed that companies would gather more personal information than they need unless policymakers set requirements for data protection. However, if regulators restricted data collection but ignored data protection, companies did not guard customers' data enough.
Data collection can be restricted:
Consumers in the digital world give away a large part of their personal data. For example, we enter our age and credit card numbers, allow companies to track our behaviour, and often display our geographical location.
However, customers are also becoming aware of the risks of their information being stolen, misused, or sold to third parties and are looking for privacy protections. A 2019 survey showed that 81 per cent of participants felt the risk of data collection outweighed the benefits.
A user's decision to participate in an online platform activity depends partly on how many people are using it. This concept is known as "network effects."
Negative network effects often drive privacy risk. The more users, the bigger the company's database, the more attractive it becomes to attack.
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