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Nowadays, mobile devices, such as smartphones, have been widely used all over the world. In addition, the performance of image recognition has drastically increased with deep learning technologies. From these backgrounds, some photo-based information services provided in a client-server architecture are getting popular: client users take a photo of a certain spot and send it to a server, while the server identifies the spot with an image recognizer and returns its related information to the users. However, this kind of client-server image recognition can cause a privacy issue because image recognition results are sometimes privacy-sensitive. To tackle the privacy issue, in this paper, we propose a framework of privacy-preserving image recognition called EnfPire, in which the server cannot uniquely determine the recognition result but client users can do so. An overview of EnfPire is as follows. First, client users extract a visual feature from their taken photo and transform it so that the server cannot uniquely determine the recognition result. Then, the users send the transformed feature to the server that returns a set of candidates of the recognition result to the users. Finally, the users compare the candidates to the original visual feature for obtaining the final result. Our experimental results demonstrate that EnfPire successfully degrades the server's spot-recognition accuracy from 99.8% to 41.4% while keeping 86.9% of the spot-recognition accuracy on the user side.