Introduction
Businesses are increasingly focusing on protecting user privacy while leveraging data for digital advertising, marketing, and customer insights. One common method to handle this balance is through the process of hashing personal information. However, it is crucial to handle customer data responsibly amidst a landscape of data breaches and regulatory scrutiny. Many organizations claim that by hashing, they render the data anonymous, reducing risks tied to personal privacy.
However, the belief that hashed data is fully anonymized is misleading and can have serious legal and ethical implications. Additionally, methods to anonymize data, including hashing, face challenges and limitations that do not fully safeguard against reidentification risks. This article explores why hashed data doesn’t truly anonymize personal data, addressing common privacy misconceptions.
Understanding Hashing: A Mathematical Overview
At its core, hashing involves taking an input (like an email address, phone number, or other personal identifier) and processing it through a hash function to produce a fixed-size string of characters. A key characteristic of a hash function is that the same input data always generates the same output. For example, if you hash the email address “example@example.com,” you’ll consistently get a unique, hashed identifier every time.
However, while the hash appears meaningless, it still corresponds directly to the original data. Hash functions are deterministic, meaning that anyone who knows the original input and the function used can easily replicate the hash. This makes hashed data vulnerable to attack, especially as computer speeds and parallel computing evolve, allowing bad actors to guess inputs faster.
Hashing vs. Encryption: Key Differences
While both hashing and encryption are used to secure personal data, they are not the same. Encryption involves transforming data into an unreadable format but with the possibility of decryption, while hashing is a one-way process.
A major misconception in data handling practices is that hashing is a foolproof way to ensure user privacy. Encrypted data can be decrypted with a key, while hashed data cannot be reversed, but that does not mean it is completely anonymous. Hashing is especially useful in contexts like password storage, where the goal is to prevent even the database administrator from knowing the password. However, when it comes to personally identifiable information (PII), such as hashed email addresses or hashed phone numbers, the risks of identifying users remain significant.
Why Hashed Data Can Still Identify Users
Despite the common belief that hashed data is anonymous, it still holds the potential to uniquely identify individuals. For instance, hashed identifiers like email addresses or IP addresses can be cross-referenced with other datasets to re-identify individuals. With the growing power of parallel computing, malicious actors can track individuals using reverse-hashing techniques or by compiling persistent unique identifiers like device identifiers and unique mobile device identifiers.
Consider digital advertisers who often rely on hashed identifiers to track users across platforms. Although the hash itself might look different on each platform, the underlying input data (e.g., an email or user ID) remains the same, enabling companies to recognize users online.
The Misconception of Anonymity in Hashed Data
When companies claim that hashing personal information makes it anonymous, they might mislead users and regulators. Hashing does not effectively make your data anonymous, as it can still lead to the unique identification of individuals. Anonymized data refers to data that cannot be traced back to an individual, even indirectly. However, in the case of hashed personal data, anyone with access to the same input data or capable of generating identical inputs can arrive at the same hash result. This means hashed data can often still be used to identify someone or track people, even though it may appear anonymized at first glance.
The key problem lies in the fact that the same input will always generate the same output. If a bad actor, for example, gets hold of a large dataset of hashed emails and has access to a list of common email addresses, they could run those addresses through the same hash function and uncover hashed email addresses in the database.
Real-World Examples of Hashing Vulnerabilities
A notable case of improper use or disclosure of hashed data occurred when some companies collected hashed email addresses and hashed phone numbers from users and used these hashed identifiers in ways that violated privacy compliance. Improper use of hashed data fails to preserve user privacy, as hashes can still potentially be reversed or linked back to the individual. Companies often believe that hashing is an excuse for improper use, assuming that because the data is hashed, it’s automatically anonymized and safe to share. However, data breaches and privacy claims brought forward by regulatory bodies, such as the Federal Trade Commission (FTC), show otherwise.
The FTC staff has taken action against companies for improper use of hashed identifiers under the premise that their data practices were not sufficiently protecting user privacy. FTC staff will remain vigilant to ensure companies comply with privacy regulations and that data security is upheld.
The Risk of Brute Force Attacks
Brute force attacks are a significant risk when dealing with hashed data. Since a hash is simply a representation of input data, attackers can use brute force techniques to systematically guess the input values until they find one that matches the hash. Modern computer speeds and parallel computing have drastically reduced the time required to perform brute-force attacks.
For instance, if a hash corresponds to a user ID or email address, attackers can use lists of commonly used email addresses and names to reverse-engineer the hash. Although the hash may appear meaningless, it could quickly become dangerous when paired with the right computational resources.
Hashed Identifiers in Digital Advertising
In the realm of digital advertising, companies often use hashed personal data to track users across different platforms and websites. They claim that hashed identifiers are privacy-friendly since they’re based on hashing and do not store actual user data in plaintext. However, hashed identifiers, like hashed email addresses or unique advertising IDs, can still be used to identify users.
When advertisers track individuals based on hashed data, they can easily recognize users online through cross-referencing with external datasets. Therefore, advertisers need to ensure that their data handling practices are transparent, lawful, and compliant with privacy protections. Claims that hashing automatically renders data anonymous may not hold up under scrutiny by regulators like the FTC.
Device Identifiers and Hashed Data
In the mobile and tech ecosystem, device identifiers, such as unique mobile device identifiers, user identifiers, or IP addresses, are often hashed to protect privacy. However, much like hashed emails, these identifiers can still be used to track people across apps and platforms. Even though a hash might look random and appear meaningless, the hashed identifier is still a persistent unique identifier, making it a useful tool for advertisers or data brokers seeking to track individuals.
For users concerned about user privacy, the fact that device-based hashes can persist across multiple services and sessions without their consent is a significant privacy risk. Companies should remain vigilant and implement operating system privacy controls to ensure users have control over their device identifiers.
Parallel Computing and Privacy Risks
With the advent of parallel computing, the ability to reverse-engineer hashed data has become more practical. Parallel computing allows multiple processors to work simultaneously on solving complex problems, such as cracking hashed datasets.
Given the potential for significant privacy risks from advanced computing techniques, organizations need to be mindful of how their hashing practices expose users to risks. Without proper protections in place, even hashed data can be vulnerable to attacks.
Persistent Unique Identifiers: A Privacy Threat
Persistent unique identifiers, such as hashed email addresses or IP addresses, are particularly troubling from a privacy standpoint because they don’t change over time. While companies may claim that hashing this sensitive data makes it anonymous, these hashed identifiers can be linked back to individuals, compromising their data privacy.
Companies should ensure that they aren’t relying solely on hashing to claim compliance with privacy protections. Informed consent is critical when collecting personal data and creating hashed identifiers for use in tracking or advertising.
Anonymized Data: The Gold Standard
The key difference between truly anonymized data and hashed data is that anonymized data cannot be traced back to an individual, even with the use of outside information. When businesses claim that hashing personal information renders it anonymized, they may be making false privacy claims.
For data to be genuinely anonymized, it should be transformed in such a way that it becomes impossible to reverse the process, even with additional information. Hashed data, on the other hand, is still tied to the original input and can often be recovered.
Ensuring Privacy Compliance with Hashed Data
To ensure privacy compliance, companies should not act as though hashing personal data absolves them from all responsibility. Hashing alone is not enough to anonymize personal data; additional measures such as informed consent and robust security practices must be implemented.
Regulators, including the Federal Trade Commission, will continue to monitor how companies use hashed data and whether their data practices align with privacy laws. Businesses that improperly disclose hashed data may face penalties for violating user privacy.
Privacy Claims and the Role of the FTC
The Federal Trade Commission (FTC) plays an important role in enforcing privacy standards in the US. The FTC has already taken action against companies that make false privacy claims or misuse hashed data.
Businesses need to be proactive about privacy compliance and data security, ensuring they do not mislead users with false claims about the anonymization of hashed identifiers. The FTC’s vigilance will only increase as the volume of personal data shared and collected by businesses grows.
Brute Force Vulnerabilities and How to Address Them
One of the significant risks with hashed data is the possibility of brute-force attacks. Given enough time and computational resources, attackers can systematically guess inputs to crack hashes. This vulnerability is compounded by advances in parallel computing, which can speed up the guessing process.
Companies can take steps to mitigate these risks, such as using salts (random data added to the input before hashing) or implementing rate limits on repeated input attempts. Still, hashing alone is insufficient to guarantee anonymity or protection.
Conclusion
Moving forward, companies must remain vigilant and understand the limits of hashed data. While it may help with data security to some extent, hashing is not a silver bullet for anonymizing personal data. Data handling practices must be updated to reflect the reality that hashed identifiers can still be used to identify individuals or track users online.
Users, too, should be aware of how their data is being used and demand transparency from companies that collect their personal information. With a better understanding and more robust privacy practices, we can achieve a more secure and private digital landscape.
