The U.S. Census Bureau is modernizing its disclosure avoidance methodology, and as such, many data products in the future will use differential privacy to protect respondents’ confidentiality. We use such algorithms to protect privacy for the Census Barriers Attitudes and Motivators Study (CBAMS), which is a nationwide survey that covers topics related to census participation. In this paper, we create a public dataset by employing two differentially private methods – the Multinomial Randomized Response Mechanism on categorical variables and the Laplace Mechanism on continuous variables. We evaluate the privacy loss under local differential privacy and then create an accuracy measure to assess the accuracy/privacy tradeoff. Lastly, we discuss some of the successes and trials of implementing differential privacy to protect confidential survey data, and we share some of the lessons we’ve learned.