As of January 1, 2020, the California Consumer Privacy Act (CCPA) came into effect and has already altered the ways companies can make use of user data.
Before the CCPA implementation, Big Data companies had the opportunity to harvest user data and use it for data science, analytics, AI, and ML projects. Through this process, consumer data was monetized without protection for privacy. With the official introduction of the CCPA, companies now have no choice but to oblige or pay the price. Therefore begging the question; Is your company compliant?
CCPA Is Proving That Privacy is not a Commodity- It’s a Right
This legislation enforces that consumers are safe from companies selling their data for secondary purposes. Without explicit permission to use data, companies are unable to utilize said data.
User data is highly valuable for companies’ analytics or monetization initiatives. Thus, risking user opt-outs can be detrimental to a company’s progressing success. By de-identifying consumer data, companies can follow CCPA guidelines while maintaining high data quality.
The CCPA does not come without a highly standardized ruleset for companies to satisfy de-identification. The law comes complete with specific definitions and detailed explanations of how to achieve its ideals. Despite these guidelines in place, and the legislation only just being put into effect, studies have found that only 8% of US businesses are CCPA compliant.
For companies that are not CCPA compliant as of yet, the time to act is now. By thoroughly understanding the regulations put out by the CCPA, companies can protect their users while still benefiting from their data.
To do so, companies must understand the significance of maintaining analytical value and the importance of adequately de-identified data. By not complying with CCPA, an organization is vulnerable to fines up to $7500 per incident, per violation, as well as individual consumer damages up to $750 per occurrence.
For perspective, after coming into effect in 2019, GDPR released that its fines impacted companies at an average of 4% of their annual revenue.
To ensure a CCPA fine is not coming your way, assess your current data privacy protection efforts to ensure that consumers:
- are asked for direct consent to use their data
- can opt-out or remove their data for analytical purposes
- data is not re-identifiable
In essence, CCPA is not impeding a company’s ability to use, analyze, or monetize data. CCPA is enforcing that data is de-identified or aggregated, and done so to the standards that its legislation requires.
Our research found that 60% of datasets believed, by companies, to be de-identified, had a high re-identification risk. There are three methods to reduce the possibility of re-identification:
- Use state-of-the-art de-identification methods
- Assess for the likelihood of re-identification
- Implement controls, so data required for secondary purposes is CCPA compliant
Read more about these effective privacy automation methods in our blog, The business Incentives to Automate Privacy Compliance under CCPA.
Manual Methods of De-Identification Are Tools of The Past
A standard of compliance within CCPA legislation involves identifying which methods of de-identification leaves consumer data susceptible to re-identification. The manual way, which is extremely common, can leave room for re-identification. By doing so, companies are making themselves vulnerable to CCPA.
Protecting data to a company’s best abilities is achievable through techniques such as k-anonymity and differential privacy. However, applying manual methods is impractical for meeting the 30-day gracing period CCPA provides or in achieving high-quality data protection.
Understanding CCPA ensures that data is adequately de-identification and has removed risk, all while meeting all legal specifications.
Achieving CCPA regulations means ditching first-generation approaches to de-identification, and adopting privacy automation defers the possibility of re-identification. Using privacy automation as a method to protect and utilize consumer’s data is necessary for successfully maneuvering the new CCPA era.
The solution of privacy automation ensures not only that user data is correctly de-identified, but that it maintains a high data quality.
CryptoNumerics as the Privacy Automation Solution
Despite CCPA’s strict guidelines, the benefits of using analytics for data science and monetization are incredibly high. Therefore, reducing efforts to utilize data is a disservice to a company’s success.
Complying with CCPA legislation means determining which methods of de-identification leave consumer data susceptible to re-identification. Manual approach methods of de-identification including masking, or tokenization, leave room for improper anonymization.
Here, Privacy Automation becomes necessary for an organization’s analytical tactics.
Privacy automation abides CCPA while benefiting tools of data science and analytics. If a user’s data is de-identified to CCPA’s standards, conducting data analysis remains possible.
Privacy automation revolves around assessment, quantification, and assurance of data. Simultaneously, a privacy automation tool measures the risk of re-identification, applying data privacy protection techniques, and providing audit reports.
A study by PossibleNow indicated that 45% of companies are in the process of preparing, but had not expected to be compliant by the CCPA’s implementation date. Putting together a privacy automation tool to better process data and prepare for the new legislation is critical in a companies success with the CCPA. Privacy automation products such as CN-Protect allow companies to succeed in data protection while benefiting from the data’s analytics. (Learn more about CN-Protect)