Trusted execution environments (TEEs) enable efficient protection of integrity and confidentiality for applications running on untrusted platforms. They have been deployed in cloud servers to attract users who have concerns on exporting data and computation. However, recent studies show that TEEs’ side channels, including memory, cache, and micro-architectural features, are still vulnerable to adversarial exploitation. As many such attacks utilize program access patterns to infer secret information , data oblivious programs have been considered a practical defensive solution. However, they are often difficult to develop and optimize via either manual or automated approaches. We present the oblivious TEE with MapReduce (TEE-MR) approach that uses application frameworks, an approach between fully manual and fully automated, to hide the details of access-pattern protection to significantly minimize developers’ efforts. We have implemented the approach with the MapReduce application framework for data-intensive applications. It can regulate application dataflows and hide application-agnostic access-pattern protection measures from developers. Compared to manual composition approaches, it demands much less effort for developers to identify access patterns and to write code. Our approach is also easy to implement, less complicated than fully automated approaches , for which we have not seen a working prototype yet. Our experimental results show that TEE-MR-based applications have good performance , comparable to those carefully developed with time-consuming manual composition approaches.

Trusted Execution Environments (TEEs) are gradually adopted by major cloud providers, offering a practical option of confidential computing for users who do not fully trust public clouds. TEEs use CPU -enabled hardware features to eliminate direct breaches from compromised operating systems or hypervisors. However, recent studies have shown that side-channel attacks are still effective on TEEs. An appealing solution is to convert applications to be data oblivious to deter many side-channel attacks. While a few research prototypes on TEEs have adopted specific data oblivious operations, the general conversion approaches have never been thoroughly compared against and tested on benchmark TEE applications. These limitations make it difficult for researchers and practitioners to choose and adopt a suitable data oblivious approach. To address these issues, we conduct a comprehensive analysis of representative conversion approaches and perform an extensive empirical study to provide insights into their performance and ease of use with benchmark TEE applications.

Trusted Execution Environments, such as Intel SGX, use hardware supports to ensure the confidentiality and integrity of applications against a compromised cloud system. However, side channels like access patterns remain for adversaries to exploit and obtain sensitive information. Common approaches use oblivious programs or primitives, such as ORAM, to make access patterns oblivious to input data, which are challenging to develop. This demonstration shows a prototype SGX-MR-Prot for efficiently protecting access patterns of SGX-based data-intensive applications and minimizing developers' efforts. SGX-MR-Prot uses the MapReduce framework to regulate application dataflows to reduce the cost of access-pattern protection and hide the data oblivious details from SGX developers. This demonstration will allow users to intuitively understand the unique contributions of the framework-based protection approach via interactive exploration and visualization.

Large training data and expensive model tweaking are common features of deep learning development for images. As a result, data owners often utilize cloud resources or machine learning service providers for developing large-scale complex models. This practice, however, raises serious privacy concerns. Existing solutions are either too expensive to be practical, or do not sufficiently protect the confidentiality of data and model. In this paper, we aim to achieve a better trade-off among the level of protection for outsourced DNN model training, the expenses, and the utility of data, using novel image disguising mechanisms. We design a suite of image disguising methods that are efficient to implement and then analyze them to understand multiple levels of tradeoffs between data utility and protection of confidentiality. The experimental evaluation shows the surprising ability of DNN modeling methods in discovering patterns in disguised images and the flexibility of these image disguising mechanisms in achieving different levels of resilience to attacks.

The development of the next-generation sequencing technology has enabled systems immunology researchers to conduct detailed immune repertoire analysis at the molecular level that allows researchers to understand the healthiness of a patient’s immune system. Recent studies have shown that the single-linkage clustering algorithm can give the best results for B cell clonality analysis – a critical type of immune repertoire sequencing (IR-Seq) analysis. Large sequence datasets (e.g., millions of sequences) are being collected to comprehensively understand how a specific person’s immune system evolves over different stages of disease development. However, the classical single-linkage clustering algorithm does not scale well to such large sequence datasets. Surprisingly, no study has been done to address this scalability issue for immunology research and development. We study three different strategies to scale up the single-linkage algorithm for sequence data. They include (1) the approximate single-linkage algorithm enhanced with the non-Euclidean indexing methods, (2) the Spark-based single-linkage algorithm (SparkMST) that was originally designed for vector data and now modified for sequence data, and (3) a new tree-based sequence summarization approach – SCT that aims to reduce the data for single-linkage clustering with well-preserved clustering quality.We have implemented these approaches and experimented with real sequence datasets for B cell clonality analysis. (1) The index-enhanced hierarchical clustering algorithm (e.g., VPT-HC using the Vantage-Point tree for indexing) preserves the clustering quality very well while significantly reducing the time complexity. (2) The SCT approach serving as a preprocessing step can effectively reduce data size for clustering. The overall clustering, SCT followed by VPT-HC, is the fastest among the evaluated single-machine algorithms. However, this approach also slightly affects the clustering quality. (3) The SparkMST parallel algorithm scales out nicely and also gives exact single-linkage clustering results. However, SparkMST is tied to the single-linkage algorithm and cannot be extended to general hierarchical clustering algorithms. Although this study focused on the specific application area: the B cell clonality analysis, we believe other sequence data analysis problems may find the developed scalable techniques useful.

Abstract Intel SGX has been a popular trusted execution environment (TEE) for protecting the integrity and confidentiality of applications running on untrusted platforms such as cloud. However, the access patterns of SGX-based programs can still be observed by adversaries, which may leak important information for successful attacks. Researchers have been experimenting with Oblivious RAM (ORAM) to address the privacy of access patterns. ORAM is a powerful low-level primitive that provides application-agnostic protection for any I/O operations, however, at a high cost. We find that some application-specific access patterns, such as sequential block I/O, do not provide additional information to adversaries. Others, such as sorting, can be replaced with specific oblivious algorithms that are more efficient than ORAM. The challenge is that developers may need to look into all the details of application-specific access patterns to design suitable solutions, which is time-consuming and error-prone. In this paper, we present the lightweight SGX based MapReduce (SGX-MR) approach that regulates the dataflow of data-intensive SGX applications for easier application-level access-pattern analysis and protection. It uses the MapReduce framework to cover a large class of data-intensive applications, and the entire framework can be implemented with a small memory footprint. With this framework, we have examined the stages of data processing, identified the access patterns that need protection, and designed corresponding efficient protection methods. Our experiments show that SGX-MR based applications are much more efficient than the ORAM-based implementations.