statistics_noise.py 文件源码

def satisfies_dp(sensitivity, epsilon, delta, std):
    '''
    Return True if (epsilon, delta)-differential privacy is satisfied.
    '''
    # find lowest value at which epsilon differential-privacy is satisfied
    lower_x = -(float(epsilon) * (std**2.0) / sensitivity) + sensitivity/2.0
    # determine lower tail probability of normal distribution w/ mean of zero
    lower_tail_prob = scipy.stats.norm.cdf(lower_x, 0, std)
    # explicitly return Boolean value to avoid returning numpy type
    if (lower_tail_prob <= delta):
        return True
    else:
        return False