No abstract

Clients of storage-as-a-service systems such as Amazon's S3 want to be sure that the files they have entrusted to the cloud are available now and will be available in the future.Using protocols from previous work on proofs of retrievability and on provable data possession, clients can verify that their files are available now. But these protocols do not guarantee that the files are replicated onto multiple drives or multiple datacenters. Such tests are crucial if cloud storage is to provide resilience to natural disasters and power outages as well as improving the network latency to different parts of the world.In this paper, we study the problem of verifying that a cloud storage provider replicates the data in diverse geolocations. We provide a theoretical framework for verifying this property. Our model accurately determines which Amazon CloudFront location serves content for Planetlab nodes across the continental US.Our work is complementary to the recent paper of Bowers et al., which uses different techniques to verify that files are replicated across multiple drives in a single datacenter.

No abstract

Background In biomedical applications, valuable data is often split between owners who cannot openly share the data because of privacy regulations and concerns. Training machine learning models on the joint data without violating privacy is a major technology challenge that can be addressed by combining techniques from machine learning and cryptography. When collaboratively training machine learning models with the cryptographic technique named secure multi-party computation, the price paid for keeping the data of the owners private is an increase in computational cost and runtime. A careful choice of machine learning techniques, algorithmic and implementation optimizations are a necessity to enable practical secure machine learning over distributed data sets. Such optimizations can be tailored to the kind of data and Machine Learning problem at hand. Methods Our setup involves secure two-party computation protocols, along with a trusted initializer that distributes correlated randomness to the two computing parties. We use a gradient descent based algorithm for training a logistic regression like model with a clipped ReLu activation function, and we break down the algorithm into corresponding cryptographic protocols. Our main contributions are a new protocol for computing the activation function that requires neither secure comparison protocols nor Yao’s garbled circuits, and a series of cryptographic engineering optimizations to improve the performance. Results For our largest gene expression data set, we train a model that requires over 7 billion secure multiplications; the training completes in about 26.90 s in a local area network. The implementation in this work is a further optimized version of the implementation with which we won first place in Track 4 of the iDASH 2019 secure genome analysis competition. Conclusions In this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure multi-party computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.

We show that no commitment scheme that is hiding and binding according to the standard definition is semantically-secure under selective opening attack (SOA), resolving a long-standing and fundamental open question about the power of SOAs. We also obtain the first examples of IND-CPA encryption schemes that are not secure under SOA, both for sender corruptions where encryption coins are revealed and receiver corruptions where decryption keys are revealed. These results assume only the existence of collision-resistant hash functions.