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In this work, we tackle the question of how to benchmark reconstruction of inputs from deep neural networks~(DNN) representations. This inverse problem is of great importance in the privacy community where obfuscation of features has been proposed as a technique for privacy-preserving machine learning~(ML) inference. In this benchmark, we characterize different obfuscation techniques and design different attack models. We propose multiple reconstruction techniques based upon distinct background knowledge of the adversary. We develop a modular platform that integrates different obfuscation techniques, reconstruction algorithms, and evaluation metrics under a common framework. Using our platform, we benchmark various obfuscation and reconstruction techniques for evaluating their privacy-utility trade-off. Finally, we release a dataset of obfuscated representations to foster research in this area. We have open-sourced code, dataset, hyper-parameters, and trained models that can be found at \url{https://tiny.cc/simba}.