PRO-ORAM: Constant Latency Read-Only Oblivious RAM

Oblivious RAM is a well-known cryptographic primitive to hide data access patterns. However, the best known ORAM schemes require a logarithmic computation time in the general case which makes it infeasible for use in real-world applications. In practice, hiding data access patterns should incur a constant latency per access. In this work, we present PRO-ORAM --- an ORAM construction that achieves constant latencies per access in a large class of applications. PRO-ORAM theoretically and empirically guarantees this for read-only data access patterns, wherein data is written once followed by read requests. It makes hiding data access pattern practical for read-only workloads, incurring sub-second computational latencies per access for data blocks of 256 KB, over large (gigabyte-sized) datasets.PRO-ORAM supports throughputs of tens to hundreds of MBps for fetching blocks, which exceeds network bandwidth available to average users today. Our experiments suggest that dominant factor in latency offered by PRO-ORAM is the inherent network throughput of transferring final blocks, rather than the computational latencies of the protocol. At its heart, PRO-ORAM utilizes key observations enabling an aggressively parallelized algorithm of an ORAM construction and a permutation operation, as well as the use of trusted computing technique (SGX) that not only provides safety but also offers the advantage of lowering communication costs

Re-aligning Shadow Models can Improve White-box Membership Inference Attacks

Machine learning models have been shown to leak sensitive information about their training datasets. As models are being increasingly used, on devices, to automate tasks and power new applications, there have been concerns that such white-box access to its parameters, as opposed to the black-box setting which only provides query access to the model, increases the attack surface. Directly extending the shadow modelling technique from the black-box to the white-box setting has been shown, in general, not to perform better than black-box only attacks. A key reason is misalignment, a known characteristic of deep neural networks. We here present the first systematic analysis of the causes of misalignment in shadow models and show the use of a different weight initialisation to be the main cause of shadow model misalignment. Second, we extend several re-alignment techniques, previously developed in the model fusion literature, to the shadow modelling context, where the goal is to re-align the layers of a shadow model to those of the target model.We show re-alignment techniques to significantly reduce the measured misalignment between the target and shadow models. Finally, we perform a comprehensive evaluation of white-box membership inference attacks (MIA). Our analysis reveals that (1) MIAs suffer from misalignment between shadow models, but that (2) re-aligning the shadow models improves, sometimes significantly, MIA performance. On the CIFAR10 dataset with a false positive rate of 1\%, white-box MIA using re-aligned shadow models improves the true positive rate by 4.5\%.Taken together, our results highlight that on-device deployment increase the attack surface and that the newly available information can be used by an attacker