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Useful tools for local differential privacy, which mainly consists of several parts:
- basis
- ldplib: local differential privacy library
- kvlib: key-value library
- mdlib: missing data library
- heavy_hitters
- hadamard_response
- k_random_response
- k_subset
- rappor
- mean_solutions
- duchi
- piecewise
The ldplib provides basic randomized functions.
- eps2p: turn the privacy budget to the probability by coin flipping
- discretization: used to discretize a continuous value
- RR: Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
- Unary Encoding: from paper Locally Differentially Private Protocols for Frequency Estimation
- SUE: symmetric unary encoding from paper Locally Differentially Private Protocols for Frequency Estimation
- OUE: optimized unary encodingfrom paper Locally Differentially Private Protocols for Frequency Estimation
Some basic encoding terms:
-
kv: a kv pair denoted as$\langle k, v\rangle$ , where$k\in {0,1}, v\in[-1,1]$ . -
kvl: a list of key-value pairs, denoted by$[\langle k_1, v_1\rangle,\langle k_2, v_2\rangle...]$ . The kvl is used to represent the $i-$th key-value or to represent a list of key-value pairs of one user. -
kvt: a$n\times d$ key-value table. A kvt is used to represent the kvl from$n$ users.
The kvlib main contains the following perturbation and analysis algorithms:
PrivBV: PrivKV: Key-Value Data Collection with Local Differential PrivacyBiSample: BiSample: Bidirectional Sampling for Handling Missing Data with Local Differential Privacy.SE: from paper Conditional Analysis for Key-Value Data with Local Differential Privacy
Hadamard Repsonse: Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communicationk-RR: the k-randomized responsek-subset:RAPPOR: RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
duchi: also known as the 1Bit Mechanism (noted that the input domain of 1Bit is [1,m], while the input domain of duchi is [-1,1]).PM: Collecting and Analyzing Multidimensional Data with Local Differential Privacy