  The wide adoption of wearable smart devices with onboard cameras greatly
increases people's concern on privacy infringement. Here we explore the
possibility of easing persons from photos captured by smart devices according
to their privacy protection requirements. To make this work, we need to address
two challenges: 1) how to let users explicitly express their privacy protection
intention, and 2) how to associate the privacy requirements with persons in
captured photos accurately and efficiently. Furthermore, the association
process itself should not cause portrait information leakage and should be
accomplished in a privacy-preserving way. In this work, we design, develop, and
evaluate a protocol, that enables a user to flexibly express her privacy
requirement and empowers the photo service provider (or image taker) to exert
the privacy protection policy.Leveraging the visual distinguishability of
people in the field-of-view and the dimension-order-independent property of
vector similarity measurement, we achieves high accuracy and low overhead.
  We implement a prototype system, and our evaluation results on both the
trace-driven and real-life experiments confirm the feasibility and efficiency
of our system.
