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28 March 2024 |
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Article overview
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Randomized Response Mechanisms for Differential Privacy Data Analysis: Bounds and Applications | Fei Ma
; Ping Wang
; | Date: |
14 Dec 2021 | Abstract: | Randomized response, as a basic building-block for differentially private
mechanism, has given rise to great interest and found various potential
applications in science communities. In this work, we are concerned with
three-elements randomized response (RR$_{3}$) along with relevant applications
to the analysis of weighted bipartite graph upon differentially private
guarantee. We develop a principled framework for estimating statistics produced
by RR$_{3}$-based mechanisms, and then prove the corresponding estimations to
be unbiased. At the same time, we study in detail several fundamental and
significant members in RR$_{3}$ family, and derive the closed-form solutions to
unbiased estimations. Next, we show potential applications of several
RR$_{3}$-based mechanisms into the estimation of average degree and average
weighted value on weighted bipartite graph when requiring local differential
privacy guarantee. In the meantime, we determine the lower bounds for choice of
relevant parameters by minimizing variance of statistics in order to design
optimal RR$_{3}$-based local differential private mechanisms, with which we
optimize previous protocols in the literature and put forward a version that
achieves the tight bound. Last but most importantly, we observe that in the
analysis of relational data such as weighted bipartite graph, a portion of
privacy budget in local differential private mechanism is sometimes "consumed"
by mechanism itself accidentally, resulting to a more stronger privacy
guarantee than we would get by simply sequential compositions. | Source: | arXiv, 2112.07397 | Services: | Forum | Review | PDF | Favorites |
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