As　a　data-driven　science,　machine　learning　requires　vast　amounts　of　training　data　and　computational　resources.　However,　for　highly　privacy-sensitive　data,　it　is　crucial　to　protect　the　privacy　of　the　data　during　both　the　training　and　utilization　of　machine　learning　models.　In　this　paper,　we　propose　a　privacy-preserving　machine　learning　approach　using　autoencoders　and　differential　privacy　mechanisms　to　safeguard　data　privacy　while　minimizing　the　impact　on　data　availability.　Specifically,　we　augment　logistic　regression　and　ResNet18　models　with　different　architectures　of　autoencoders　to　perform　data　encryption?　without　compromising　the　machine　learning　tasks.　Additionally,　we　employ　differential　privacy　mechanisms　to　introduce　gradient　perturbations　in　the　encoding　part　of　the　autoencoder,　enhancing　the　algorithm’s　security　and　further　protecting　data　privacy.　We　also　design　the　cosine　similarity　between　the　encoded　and　original　data　as　a　metric　for　evaluating　data　privacy,　considering　model　performance,　privacy　budget,　and　data　privacy　collectively　to　balance　data　availability　and　privacy.　Extensive　experiments　conducted　on　MNIST,　CIFAR-10,　PathMNIST,　and　BloodMNIST　datasets　demonstrate　that　for　simple　logistic　regression　models　handling　easily　classifiable　datasets,　employing　simple　autoencoder　structures　can　enhance　classification　accuracy,　with　significant　performance　impact　after　adding　differential　privacy.　For　ResNet18,　utilizing　convolutional　autoencoders　for　data　encryption　generally　has　minimal　impact　on　model　classification　performance　and　can　even　improve　accuracy　in　most　cases.　Adding　differential　privacy　has　minor　effects　on　model　classification　performance.　Selecting　appropriate　model　structures　and　privacy　budgets　for　different　usage　scenarios　can　ensure　both　data　availability　and　privacy.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.