noise

type ConfidenceInterval struct {
	LowerBound, UpperBound float64
}

type Kind int

type Noise interface {
	// AddNoiseInt64 noise to the specified int64 x so that the output is ε-differentially
	// private given the L_0 and L_∞ sensitivities of the database.
	AddNoiseInt64(x, l0sensitivity, lInfSensitivity int64, epsilon, delta float64) int64

	// AddNoiseFloat64 noise to the specified float64 x so that the output is ε-differentially
	// private given the L_0 and L_∞ sensitivities of the database.
	AddNoiseFloat64(x float64, l0sensitivity int64, lInfSensitivity, epsilon, delta float64) float64

	// Threshold returns the smallest threshold k needed in settings where the Noise instance
	// is used to achieve differential privacy on histograms where the inclusion of histogram
	// partitions depends on which privacy units are present in the database.
	//
	// Inputs:
	//   l0Sensitivity: The maximum number of partitions that a privacy unit can contribute to.
	//   lInfSensitivity: How much any single partition's value can change from
	//     the contribution of a single privacy unit. When adding a privacy unit results in the
	//     creation of a new partition, this bounds the magnitude of that partition.
	//   epsilon: The parameter ε passed to AddNoise.
	//   noiseDelta: The parameter δ passed to AddNoise.
	//   thresholdDelta: Differential privacy loss (0, delta) incurred by thresholding,
	//     i.e. the probability to output a partition that only has one privacy unit in it.
	//
	// More precisely, Threshold returns the smallest k such that the following algorithm:
	//
	//   func Histogram(records []struct{key string, value float64}) map[string]float64 {
	//     sums := make(map[string]float64)
	//	   for _, record := range records {
	//       sums[record.key] = sums[record.key]+record.value
	//     }
	//     noisySums := make(map[string]float64)
	//     for key, sum := range sums {
	//       noisySum := AddNoiseFloat64(sum, sensitivity, epsilon, noiseDelta)
	//       if noisySum ≥ k:
	//         noisySums[key] = noisySum
	//     }
	//     return noisySums
	//   }
	//
	// satisfies (epsilon,noiseDelta+thresholdDelta)-differential privacy under the
	// given assumptions of L_0 and L_∞ sensitivities.
	Threshold(l0Sensitivity int64, lInfSensitivity, epsilon, noiseDelta, thresholdDelta float64) float64

	// ComputeConfidenceIntervalInt64 computes a confidence interval that contains the raw integer value x from which int64
	// noisedX is computed with a probability greater or equal to 1 - alpha based on the specified noise parameters.
	ComputeConfidenceIntervalInt64(noisedX, l0Sensitivity, lInfSensitivity int64, epsilon, delta, alpha float64) (ConfidenceInterval, error)

	// ComputeConfidenceIntervalFloat64 computes a confidence interval that contains the raw value x from which float64
	// noisedX is computed with a probability equal to 1 - alpha based on the specified noise parameters.
	ComputeConfidenceIntervalFloat64(noisedX float64, l0Sensitivity int64, lInfSensitivity, epsilon, delta, alpha float64) (ConfidenceInterval, error)
}