Dear farms, we know your data is invaluable to your operations. As companies scramble to gain insights that will give them a competitive edge, it's more important than ever to keep your farming data safe and private. But you don't have to sacrifice insights for privacy. There are solutions that can help you anonymize your data safely, while still enabling you to proactively identify trends and make better decisions.
Let's get you up to speed on data anonymization.
Data anonymization is defined as the process of altering personally identifiable information within data sets so that the companies (or individuals) whom the data describe remain anonymous. This technique involves changing or masking original data, making it impossible to trace back to the source or identify the individuals, thereby ensuring privacy and confidentiality while still allowing for the use and analysis of the data.
Let’s first start with a simple example, but in the best way possible. With (of course) chocolate chip cookies.
Chocolate Chip Cookie Data Analogy 🍪
Imagine that you have a secret recipe for the world's best chocolate chip cookies. You want to share your recipe with other bakers, but you don't want anyone to know who you are or where you live. So, you decide to anonymize your recipe.
To do this, you remove all of the personally identifiable information from your recipe. You don't include your name, address, or phone number. You also remove any other information that could identify you, such as the brand of flour you use or the type of oven you have.
Once your recipe is anonymized, you can share it with other bakers without worrying about your privacy. They can still use your recipe to make delicious chocolate chip cookies, but they won't be able to track down your secret stash of chocolate chips.
Data anonymization works in a similar way. It removes all of the personally identifiable information from a dataset, so that the data can be shared and used without revealing the identity of the individuals in the dataset.
Yes, the cookie example is kind of silly, but you can easily see how it translates. The next question you need to ask yourself though is:
Why is data anonymization so critically important (to aquaculture) right now?
Glad you asked! 😉
The aquaculture industry needs predictive modeling platforms (like Harpoon) to accelerate the science of aquaculture. Data Intelligence platforms, like Manolin, can answer those lingering questions farms want to know faster than ever with AI.
The increasing reliance on AI is primed to drive further advancements in the field. Data privacy is critical in the age of AI because machine learning models are trained on large amounts of data. This data (of course) contains sensitive information, that farms may see as intellectual property or trade secrets.
Data anonymization is the key solution, enabling farms to make their data accessible to AI without revealing sensitive information. This allows for untapped insights and collaboration, while also retaining company IP, giving the industry the ability to scale research questions faster than ever before.
Here's why anonymization is the Gold Standard for the future of aquaculture data processing.
Anonymization stands as the gold standard for the future of aquaculture data processing. At Manolin, we recognize the critical role of data anonymization in our information processing systems, especially when handling sensitive information from farms. Our approach ensures thorough protection of your data from the outset, serving as a foundational measure to safeguard private farm data across the entire industry. This approach plays a crucial role in strengthening the intellectual property defenses of farms. Simultaneously, it fosters a collaborative environment where collective scientific advancement is encouraged, all without compromising on confidentiality.
Let's dive into the details a little more for fun 🤓
K-anonymity is one of the most common data anonymization techniques. It ensures that each record in a dataset is indistinguishable from at least k-1 other records. Let's translate that to less technical jargon.
Let's imagine a dataset of 10 farms where each row in the dataset contains the following metrics representing the end of its most recent production cycle. ("farm name", "farm location", "mortality percent").
If we look at the metrics there are 2 things that can help us identify the location "farm name" and "farm location". When we remove these only "mortality percent" remains. At first glance this may seem like our job is done however we probably want to consider one more scenario. Out of those 10 farms 1 is a part of a publicly traded company. We need to ensure that the numbers that they report in the publicly available financial reports can not be traced back to the original farm.
So we have one last step. We will round the mortality percent down to the nearest number. So if our 10 mortality numbers are as follows (8.7, 8.2, 8.3, 22.1, 20.3, 20.7, 20.3, 22.1, 23.1, 23.1) we would convert these to be (8.0, 8.0, 8.0, 22.0, 20.0, 20.0, 20.0, 22.0, 23.0, 23.0).
Now with our final set of data have the following samples.
3 farms -> 8.0
2 farms -> 22.0
3 farms -> 20.0
2 farms -> 23.0
This means the data points have been generalized enough that there are at least two sets of every combination of data in the data set so our k-score = 2. Of course when Manolin is conducting studies we strive for much higher k-scores but this helps demonstrate how k-anonymity is used in practice!
Utilizing a data intelligence platform that incorporates data anonymization presents fish farms with a unique opportunity to enhance data security while actively participating in collective industry growth. By anonymizing data, farms ensure their sensitive operational information remains untraceable, preserving their competitive edge and intellectual property. This secure platform not only protects individual data but also encourages a collaborative approach to industry benchmarking. Contributing to a larger, anonymized data pool allows for a comprehensive understanding of market trends and comparative performance metrics, enabling informed decision-making based on overarching industry insights.
Moreover, the participation in such platforms allows fish farms to access customized analytics and reports, offering strategic value drawn from a broader data set than available in their silos. These insights are crucial for improving operational efficiencies, optimizing practices in areas like feed management, health protocols, and environmental sustainability. Furthermore, sharing anonymized data bolsters research efforts into critical areas such as fish health and environmental impact, promoting industry-wide innovation and sustainable practices. Through this collaborative and secure data sharing, fish farms don't just gain individual advantages—they also contribute significantly to the collective advancement and resilience of the aquaculture industry.
Key Benefits of Data Anonymization:
Enhanced data security
Collective industry benchmarking
Improved operational efficiency
The protection of farm privacy within Manolin's datasets is not just a priority but a core principle of our platform. Our approach provides comprehensive data protection right from the beginning, establishing a strong foundation to secure confidential farm data throughout the industry.
Get all the benefits of data anonymization in action through Manolin's free Researcher Tier.
By signing up for Manolin Researcher, farms can now get unique personalized reports for every sponsored study on Manolin, all for free, and with the ability to control what sponsored research studies they can choose to contribute towards.
Simply sign up for our Researcher Tier now.