Businesses are becoming increasingly reliant on data to make decisions and learn about the market. Yet, due to an increase in regulations, the information they have collected is becoming less and less useful. While people have been quick to blame privacy laws, in reality, the biggest impediment to analytics and data science are insufficient data privacy solutions.
From our market research, the top four things people are doing are (1) access controls, (2) masking, (3) encryption, and (4) tokenization. While these solutions are a step in the right direction, they wipe the data of its value and leave businesses open to regulatory penalties and reputational damage.
Your data privacy solutions are insufficient
Access controls: Access controls limit who can access data. While important, they are just not an effective privacy-preserving strategy because the controls do not protect the identity of the individuals or prevent their data from being used for purposes they have not consented to. It is a an all-or-nothing approach, whereby someone has access to the data, and privacy is not protected, or not, in which case, no insights can be gleaned at all.
Masking: This is a process by which sensitive information is replaced with synthetic data. In doing so, the analytical value is wiped. While this solution works for testing, it is not an advantageous solution if you are planning to provide the data to data scientists. After all, you are sending them this data to unlock valuable insights!
Encryption: Encryption is a security mechanism that protects data until it is used. At which point, the data is decrypted, exposing the private data to the user. Additionally, the concern with encryption, is that if someone accesses the key, they can reverse the entire process (decryption), putting the data at risk.
Tokenization: Tokenization, also known as pseudonymization, is the process of encoding direct identifiers, like email addresses, into another value (token) and keeping the original mapping of token stored somewhere for relinking in the future. When businesses employ this technique, they leave the indirect identifiers (quasi-identifiers) as they are. Yet, combinations of quasi-identifiers are a proven method to re-identify individuals in a dataset.
Such a risk emphasizes the importance of understanding the re-identification risk of a dataset when comparing the effects of your organizations’ privacy protection actions. Moreover, this process is often reversed to perform analysis -violating the very principle of the process. The most important question to ask yourself is how do I know my datasets have been anonymized? If you only implement tokenization, the answer is you don’t.
Risk-aware anonymization will unlock the value of your data.
To unlock the value of your datasets in the regulatory era, businesses should implement privacy techniques. And many have! However, as we’ve discussed, the commonly used techniques are insufficient to preserve analytical value and protect your organization. The only way data will be useful to your data scientists is if you transform the data in such a way that the privacy elements enabling re-identification are removed while degrading the data as little as possible.
Consequently, businesses must prioritize risk-aware anonymization in order to optimize the reduction of re-identification risk and protect the value of data.
CN-Protect is the ideal solution to achieve your goals. It utilizes AI and advanced privacy protection methods, like differential privacy and k-anonymization, to assess, quantify and assure privacy and insights are produced in unison.
The process is as follows:
- Classify metadata: identify the direct, indirect, and sensitive data in an automated manner, to help businesses understand what kind of data they have.
- Quantify risk: calculate the risk of re-identification of individuals and provide a privacy risk score.
- Protect data: apply advanced privacy techniques, such as k-anonymization and differential privacy, to tables, text, images, video, and audio. This involves optimizing the tradeoff between privacy protection (removing elements that constitute privacy risk) and analytical value (retaining elements that constitute data fidelity)
- Audit-ready reporting: keep track of what the dataset is, what kind of privacy-protecting transformations were applied, changes in the risk score (before and after privacy actions have been applied), who applied the transformation and at what time, and where the data went. This is the key piece to proving data has been defensibly anonymized to regulatory authorities.
In doing so, businesses are able to establish the privacy-protection of datasets to a standard that fulfills data protection regulations, protects you from privacy risk, and most importantly, preserves the value of the data. In essence, it will unlock data that was previously restricted, and help you achieve improved data-driven outcomes by protecting data in an optimized manner.
By measuring the risk of identification, applying privacy-protection techniques, and providing audit reports throughout the whole process, CN-Protect is the only data privacy solution that will comprehensively unlock the value of your data.