Privacy is crucial for businesses looking to expand their operations using AI and Deep Learning. Recently, researchers at Apple, in their new paper titled "Bytes Are All You Need: Transformers Operating Directly On File Bytes", propose a novel deep learning model called ByteFormer that can perform classification directly on file bytes without the need for decoding files at inference time. This approach enables the development of models that can operate on multiple input modalities and has applications in privacy-preserving inference. By applying a permutation at random before training and optionally applying uniform noise, ByteFormer can perform inference on obfuscated inputs with no loss of accuracy. For example, a smart-home device that performs inference on RGB images can compromise user privacy if an adversary accesses this model input. However, this model can perform inference on privacy-preserving inputs, ensuring data privacy for users. This novel deep-learning method has the potential to revolutionize how businesses leverage data. We highly recommend giving it a read to stay ahead of the curve! 🔗 lnkd.in/dTk-45KW #privacy #AI #deeplearning #DataSecurity #innovation #DataPrivacy #AIResearch #PrivacyPreserving #DataLeveraging #Technology #ByteClassification #DataInference #FileBytes #Transformers #AppleResearch