  Among existing privacy-preserving approaches, Differential Privacy (DP) is a
powerful tool that can provide privacy-preserving noisy query answers over
statistical databases and has been widely adopted in many practical fields. In
particular, as a privacy machine of DP, Randomized Aggregable
Privacy-Preserving Ordinal Response (RAPPOR) enables strong privacy, efficient,
and high-utility guarantees for each client string in data crowdsourcing.
However, as for Internet of Things(IoT), such as smart gird, data are often
processed in batches. Therefore, developing a new random response algorithm
that can support batch-processing tend to make it more efficient and suitable
for IoT applications than existing random response algorithms. In this paper,
we propose a new randomized response algorithm that can achieve
differential-privacy and utility guar-antees for consumer's behaviors, and
process a batch of data at each time. Firstly, by applying sparse coding in
this algorithm, a behavior signature dictionary is created from the aggregated
energy consumption data in fog. Then, we add noise into the behavior signature
dictionary by classical randomized response techniques and achieve the
differential privacy after data re-aggregation. Through the security analysis
with the principle of differential privacy and experimental results
verification, we find that our Algorithm can preserve consumer's privacy
with-out comprising utility.
