  Large organizations such as social media companies continually release data,
for example user images. At the same time, these organizations leverage their
massive corpora of released data to train proprietary models that give them an
edge over their competitors. These two behaviors can be in conflict as an
organization wants to prevent competitors from using their own data to
replicate the performance of their proprietary models. We solve this problem by
developing a data poisoning method by which publicly released data can be
minimally modified to prevent others from train-ing models on it. Moreover, our
method can be used in an online fashion so that companies can protect their
data in real time as they release it.We demonstrate the success of our approach
onImageNet classification and on facial recognition.
