  In order to remain competitive, Internet companies collect and analyse user
data for the purpose of improving user experiences. Frequency estimation is a
widely used statistical tool which could potentially conflict with the relevant
privacy regulations. Privacy preserving analytic methods based on differential
privacy have been proposed, which either require a large user base or a trusted
server; hence may give big companies an unfair advantage while handicapping
smaller organizations in their growth opportunity. To address this issue, this
paper proposes a fair privacy-preserving sampling-based frequency estimation
method and provides a relation between its privacy guarantee, output accuracy,
and number of participants. We designed decentralized privacy-preserving
aggregation mechanisms using multi-party computation technique and established
that, for a limited number of participants and a fixed privacy level, our
mechanisms perform better than those that are based on traditional perturbation
methods; hence, provide smaller companies a fair growth opportunity. We further
propose an architectural model to support weighted aggregation in order to
achieve higher accuracy estimate to cater for users with different privacy
requirements. Compared to the unweighted aggregation, our method provides a
more accurate estimate. Extensive experiments are conducted to show the
effectiveness of the proposed methods.
