Approximate computing, an advanced computational technique which returns inaccurate but acceptable results instead of exact results, has emerged as a new preferable paradigm over traditional computing architectures for energy efficient system designs. It is crucial for nanoscale integrated circuits (ICs) to achieve high speed and low power, where some intrinsic errors are acceptable, such as (deep-) machine learning, image processing, communication and other error-tolerant and cognitive applications. However, approximate computing also introduces security vulnerabilities mainly due to the uncertainty and unpredictability of intrinsic errors during approximate execution which may be indistinguishable using malicious modification of the accurate result. On the other hand, interestingly, approximate computing can also provide new approaches for security. Existing literature in approximate computing covers threat models, countermeasures, and evaluations, but lacks a framework for analysis and comparison. In this paper, we provide a classification of the state of the art in this research field, including threat models in approximate computing and promising security approaches using approximate computing.