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Given a private string q and a remote server that holds a set of public documents D, how can one of the K most relevant documents to q in D be selected and viewed without anyone (not even the server) learning anything about q or the document? This is the oblivious document ranking and retrieval problem. In this paper, we describe Coeus, a system that solves this problem. At a high level, Coeus composes two cryptographic primitives: secure matrix-vector product for scoring document relevance using the widely-used term frequency-inverse document frequency (tf-idf) method, and private information retrieval (PIR) for obliviously retrieving documents. However, Coeus reduces the time to run these protocols, thereby improving the user-perceived latency, which is a key performance metric. Coeus first reduces the PIR overhead by separating out private metadata retrieval from document retrieval, and it then scales secure matrix-vector product to tf-idf matrices with several hundred billion elements through a series of novel cryptographic refinements. For a corpus of English Wikipedia containing 5 million documents, a keyword dictionary with 64K keywords, and on a cluster of 143 machines on AWS, Coeus enables a user to obliviously rank and retrieve a document in 3.9 seconds---a 24x improvement over a baseline system. 

The unique features of blockchains such as immutability, transparency, provenance, and authenticity have been used by many large-scale data management systems to deploy a wide range of distributed applications including supply chain management, healthcare, and crowdworking in permissioned settings. Unlike permissionless settings, e.g., Bitcoin, where the network is public, and anyone can participate without a specific identity, a permissioned blockchain system consists of a set of known, identified nodes that might not fully trust each other. While the characteristics of permissioned blockchains are appealing to a wide range of largescale data management systems, these systems, have to satisfy four main requirements: confidentiality, verifiability, performance, and scalability. Various approaches have been developed in industry and academia to satisfy these requirements with varying assumptions and costs. The focus of this tutorial is on presenting many of these techniques while highlighting the trade-offs among them. We demonstrate the practicality of such techniques in real-life by presenting three different applications, i.e., supply chain management, large-scale databases, and multi-platform crowdworking environments, and show how those techniques can be utilized to meet the requirements of such applications 

Metadata from voice calls, such as the knowledge of who is communicating with whom, contains rich information about people’s lives. Indeed, it is a prime target for powerful adversaries such as nation states. Existing systems that hide voice call metadata either require trusted intermediaries in the network or scale to only tens of users. This paper describes the design, implementation, and evaluation of Addra, the first system for voice communication that hides metadata over fully untrusted infrastructure and scales to tens of thousands of users. At a high level, Addra follows a template in which callers and callees deposit and retrieve messages from private mailboxes hosted at an untrusted server. However, Addra improves message latency in this architecture, which is a key performance metric for voice calls. First, it enables a caller to push a message to a callee in two hops, using a new way of assigning mailboxes to users that resembles how a post office assigns PO boxes to its customers. Second, it innovates on the underlying cryptographic machinery and constructs a new private information retrieval scheme, FastPIR, that reduces the time to process oblivious access requests for mailboxes. An evaluation of Addra on a cluster of 80 machines on AWS demonstrates that it can serve 32K users with a 99-th percentile message latency of 726 ms—a 7✕ improvement over a prior system for text messaging in the same threat model. 

Significant amounts of data are currently being stored and managed on third-party servers. It is impractical for many small scale enterprises to own their private datacenters, hence renting third-party servers is a viable solution for such businesses. But the increasing number of malicious attacks, both internal and external, as well as buggy software on third-party servers is causing clients to loose their trust in these external infrastructures. While small enterprises cannot avoid using external infrastructures, they need the right set of protocols to manage their data on untrusted infrastructures. In this paper, we propose TFCommit, a novel atomic commitment protocol that executes transactions on data stored across multiple untrusted servers. To our knowledge, TFCommit is the first atomic commitment protocol to execute transactions in an untrusted environment without using expensive Byzantine replication. Using TFCommit, we propose an auditable data management system, Fides, residing completely on untrustworthy infrastructure. As an auditable system, Fides guarantees the detection of potentially malicious failures occurring on untrusted servers using tamper-resistant logs with the support of cryptographic techniques. The experimental evaluation demonstrates the scalability of our approach and the relatively low overhead of executing transactions on untrusted infrastructure.