It’s increasingly common for people from various organizations to work together toward shared goals. When they do, data collaboration can allow participants to learn insights faster, uncover informative trends, better understand stakeholders and more. However, people must take a privacy-first approach. That might mean using artificial intelligence (AI), creating AI-powered data collaboration tools or following more traditional best practices. Here are some excellent starting points.
Anonymize the Data Correctly
Anonymizing data is a practical way to protect people’s privacy. Doing so is particularly important if the information is extremely sensitive in its content, such as relating to a person’s health. However, some people don’t realize their attempts at data anonymization may be inadequate.
For example, under the General Data Protection Regulation, data anonymization must fulfill two goals to protect privacy sufficiently. First, it must occur in an irreversible manner. Next, the anonymization must be so effective that identifying the subject is impossible or highly impractical.
It’s likely insufficient to remove only people’s names from data. That’s a start, but those parties may still be identifiable due to the patterns that inevitably emerge in daily or otherwise frequent activities. Someone might remain identifiable due to complementing data about their locations, travel habits, length and nature of phone calls, or what they buy and when.
All parties involved in data-sharing efforts should research what steps to take to anonymize data and agree on a process that will maintain the necessary standards. An AI-powered data collaboration tool could also help, particularly if it flags instances when people do not adhere to established practices to safeguard privacy.
Follow Cybersecurity Best Practices
Another thing for everyone to keep in mind is specific types of data are particularly enticing to cybercriminals. Imagine a scenario where automobile manufacturers partner with dealerships to learn the top reasons people bring their vehicles in for repairs.
The manufacturing industry is often more vulnerable to cyberattacks because its preventive measures are comparatively less robust than other sectors. Another vulnerability is manufacturers often work with external service providers that receive or transmit clients’ data. However, they don’t always properly vet those third parties before agreeing to work with them.
People in all industries who participate in data collaboration projects must actively work to reduce cybersecurity risks. That means setting strong, unique passwords, understanding phishing attack methods and never sharing sensitive information with unknown parties.
It’s also necessary to identify network weaknesses hackers could exploit while trying to steal data. Organizations pursuing AI-powered data collaboration methods may use algorithms to detect unusual network traffic. Then, people could get alerts sooner about cybercriminals who have infiltrated or are trying to infiltrate networks to take information.
Understand the Pros and Cons of AI-Powered Data Collaboration
Many parties who collaborate while working with data make their activities safer by using privacy-enhancing technologies, including those that use AI. Some of these options protect information while people use it and when it is in transit or storage.
However, other privacy-enhancing technologies exist, too. They encompass a broad category that includes encryption, secure execution environments and more. People must understand the potential risks of AI-powered data collaboration that could make them unwittingly erode privacy. They should also stay open to other options that may be safer or more effective.
For example, generative AI chatbots can help people make sense of complex data or provide them with summaries of dense material. However, many users don’t realize companies could become liable if employees feed personally identifiable information into them. The tech executives behind those similar tools rely on users’ inputs to train their algorithms. That data collection usually happens by default and many people don’t know how to tweak settings to disallow it.
Some of the most worthwhile privacy-related tech investments take significant time to build and implement. Companies can keep data private by using platforms that allow analysis without sacrificing confidentiality.
An ideal approach is for everyone working on data collaboration projects to determine the potential privacy threats and how technology could mitigate them. Then, they could identify the possible ways forward and whether they must hire external experts to build the required technology.
Establish the Reasons for Data Collaboration
Before participating in any data collaboration effort, people must identify and understand why they will share the information. What are the primary goals, and how can sharing eliminate or reduce existing barriers? Once the relevant individuals iron out those specifics, they can create documentation that assures the public of responsible data-handling methods promoting privacy.
A 2022 study found 95% of participants did not want corporations profiting from their data. Another takeaway was many of those polled wanted to stop data usage from hurting disadvantaged or minority groups. When people who intend to engage in data collaboration take time to clarify why they need information and how having it will help, individuals will feel more open to making their details available.
They may be particularly likely to do so when clear advantages exist. Perhaps a person could accelerate the necessary eligibility checks for government benefits if they consent to one federal agency sharing their details with others. Alternatively, a patient with a rare disease might get the appropriate treatment sooner if they allow a physician to share their data with specialists or hospitals located in other states.
As people develop their data collaboration ideals, they must find the best ways to keep information safe without introducing prohibitively cumbersome restrictions. One popular approach is to create role-based information access. Then, a person can only see data associated with their jobs. That option strengthens privacy by ensuring individuals cannot get more information than they need.
Prioritize Privacy at Every Opportunity
Regardless of a planned data collaboration effort’s scope or type, people must keep privacy a top-of-mind concern. That’s essential for minimizing risk and ensuring data gets used responsibly, as the parties who have provided the information expect.
As people share information, they’ll inevitably learn new ways to keep the data even safer. Thus, individuals should always stay open to continuous improvement. Even though some changes are challenging, they’re worth the effort if they strengthen privacy.
The post What Are the Best Ways to Preserve Privacy in Data Collaboration Projects? appeared first on Datafloq.
