1 research outputs found
Probabilistic Counters for Privacy Preserving Data Aggregation
Probabilistic counters are well known tools often used for space-efficient
set cardinality estimation. In this paper we investigate probabilistic counters
from the perspective of preserving privacy. We use standard, rigid differential
privacy notion. The intuition is that the probabilistic counters do not reveal
too much information about individuals, but provide only general information
about the population. Thus they can be used safely without violating privacy of
individuals. It turned out however that providing a precise, formal analysis of
privacy parameters of probabilistic counters is surprisingly difficult and
needs advanced techniques and a very careful approach.
We demonstrate also that probabilistic counters can be used as a privacy
protecion mechanism without any extra randomization. That is, the inherit
randomization from the protocol is sufficient for protecting privacy, even if
the probabilistic counter is used many times. In particular we present a
specific privacy-preserving data aggregation protocol based on a probabilistic
counter. Our results can be used for example in performing distributed surveys