  In today's digital world, personal data is being continuously collected and
analyzed without data owners' consent and choice. As data owners constantly
generate data on their personal devices, the tension of storing private data on
their own devices yet allowing third party analysts to perform aggregate
analytics yields an interesting dilemma.
  This paper introduces PAS-MC, the first practical privacy-preserving and
anonymity stream analytics system. PAS-MC ensures that each data owner locally
privatizes their sensitive data before responding to analysts' queries. PAS-MC
also protects against traffic analysis attacks with minimal trust
vulnerabilities.We evaluate the scheme over the California Transportation
Dataset and show that we can privately and anonymously stream vehicular
location updates yet preserve high accuracy.
