receiving a request from a client device to perform a predictive analytics query on a set of data stored by a database, the request identifying a target accuracy and a maximum privacy spend;

performing the predictive analytics query on the set of data to produce a result;

perturbing the result to produce a differentially private result by injecting a noise value into the differentially private result, the noise value being sampled from a first probability distribution based on a fractional privacy spend, the fractional privacy spend comprising a first fraction of the maximum privacy spend;

iteratively calibrating the noise value of the differentially private result based on a secondary distribution different from the first probability distribution and based on a new fractional privacy spend, the new fractional privacy spend comprising a second fraction of the maximum privacy spend, the new fractional privacy spend being larger than fractional privacy spends of preceding iterations, the iterations of calibrating the noise value occurring until a relative error of the differentially private result is less than or equal to the target accuracy; and

sending, to the client device, the differentially private result.