Another way data scientists can safeguard their customer or business data is to keep the data inside the database rather than exporting a subset or local copy of the data onto their computer or device..Nowadays, there are many tools to connect different database providers to R or Python, such as the odbcConnect() function as part of the RODBC library in R, which reads and queries data from a database using an ID and password rather than importing data from a local computer..The ID and password can be removed from the R or Python file once the user has finished working with the data, so an attacker cannot run the script to get the data without a login..Also, if an attacker were to crack open a user’s personal laptop, he or she would not find a local copy of the data on that device..Row and column access is another example of data security through fine grained access controls..This mechanism masks certain columns or rows to different users..These masked columns or rows in tabled data usually contain sensitive or personally identifiable information..For example, the columns which contain financial information might be masked from the data science team but not from the finance/payments processing team..Conclusion & Other Tips Other ways to safely deal with sensitive and personally identifiable information include differential privacy and k-anonymity..To learn about these techniques, please read Dealing with data privacy – anonymization techniques.. More details