There is growing awareness that the analysis of personal data, such as individuals' mobility, financial, and health data, can provide significant benefits to society. However, liberal societies have so far refrained from such analytics, arguably due to the lack of secure analytics platforms that scale to billions of records while operating in a very strong threat model. We contend that one fundamental gap here is the lack of an architecture that can scale (actively-)secure multi-party computation (MPC) horizontally without weakening security. To bridge this gap, we present CoVault, an analytics platform that leverages server-aided MPC and trusted execution environments (TEEs) to colocate the MPC parties in a single datacenter without reducing security, and scales MPC horizontally to the datacenter's available resources. CoVault scales well empirically. For example, CoVault can scale the DualEx 2PC protocol [60] to perform epidemic analytics for a country of 80M people (about 11.85B data records/day) on a continuous basis using one core pair for every 30,000 people.

We introduce QuARC, Quantum Adaptive Routing using Clusters, a novel clustering-based entanglement routing protocol that leverages redundant, multi-path routing through multi-particle projective quantum measurements to enable high-throughput, low-overhead, starvation-free entanglement distribution. At its core, QuARC periodically reconfigures the underlying quantum network into clusters of different sizes, where each cluster acts as a small network that distributes entanglement across itself, and the end-to-end entanglement is established by further distributing between clusters. QuARC does not require a-priori knowledge of any physical parameters, and is able to adapt the network configuration using static topology information, and using local (within-cluster) measurements only. We present a comprehensive simulation-based evaluation that shows QuARC is robust against changes to physical network parameters, and maintains high throughput without starvation even as network sizes scale and physical parameters degrade.

Many types of analytics on personal data can be made differentially private, thus alleviating concerns about the privacy of individuals. However, no platform currently exists that can technically prevent data leakage and misuse with minimal trust assumptions; as a result, analytics that would be in the public interest are not done in liberal societies. To bridge this gap, we present secure selective analytics (SSA) , where data sources can a priori restrict the use of their data to a pre-defined set of privacy-preserving analytics queries performed by a specific group of analysts, and for a limited period. Furthermore, we show that a scalable SSA platform can be built in a strong threat model based on minimal trust . Technically, our SSA platform, CoVault, relies on a minimal trust implementation of functional encryption (FE), using a combination of secret sharing, secure multi-party computation (MPC), and trusted execution environments (TEEs). CoVault tolerates the compromise of a subset of TEE implementations as well as side channels. Despite the high cost of MPC, we show that CoVault scales to very large databases us-ing map-reduce-based query parallelization. For example, we show that CoVault can perform queries relevant to epidemic analytics for a country of 80M using about 8000 cores, which is tolerable given the high value of such analytics.

Analytics on personal data, such as individuals' mobility, financial, and health data can be of significant benefit to society. Such data is already collected by smartphones, apps and services today, but liberal societies have so far refrained from making it available for large-scale analytics. Arguably, this is due at least in part to the lack of an analytics platform that can secure data through transparent, technical means (ideally with decentralized trust), enforce source policies, handle millions of distinct data sources, and run queries on billions of records with acceptable query latencies. To bridge this gap, we present an analytics platform called CoVault which combines secure multi-party computation (MPC) with trusted execution environment (TEE)-based delegation of trust to be able execute approved queries on encrypted data contributed by individuals within a datacenter to achieve the above properties. We show that CoVault scales well despite the high cost of MPC. For example, CoVault can process data relevant to epidemic analytics for a country of 80M people (about 11.85B data records/day) on a continuous basis using a core pair for every 20,000 people. Compared to a state-of-the-art MPC-based platform, CoVault can process queries between 7 to over 100 times faster, as well as scale to many sources and big data.

For fear of retribution, the victim of a crime may be willing to report it only if other victims of the same perpetrator also step forward. Common examples include 1) identifying oneself as the victim of sexual harassment, especially by a person in a position of authority or 2) accusing an influential politician, an authoritarian government, or ones own employer of corruption. To handle such situations, legal literature has proposed the concept of an allegation escrow: a neutral third-party that collects allegations anonymously, matches them against each other, and de-anonymizes allegers only after de-anonymity thresholds (in terms of number of co-allegers), pre-specified by the allegers, are reached. An allegation escrow can be realized as a single trusted third party; however, this party must be trusted to keep the identity of the alleger and content of the allegation private. To address this problem, this paper introduces Secure Allegation Escrows (SAE, pronounced say). A SAE is a group of parties with independent interests and motives, acting jointly as an escrow for collecting allegations from individuals, matching the allegations, and de-anonymizing the allegations when designated thresholds are reached. By design, SAEs provide a very strong property: No less than a majority of parties constituting a SAE can de-anonymize or disclose the content of an allegation without a sufficient number of matching allegations (even in collusion with any number of other allegers). Once a sufficient number of matching allegations exist, the join escrow discloses the allegation with the allegers' identities. We describe how SAEs can be constructed using a novel authentication protocol and a novel allegation matching and bucketing algorithm, provide formal proofs of the security of our constructions, and evaluate a prototype implementation, demonstrating feasibility in practice.

ions imply choice, one that OS designers must confront when designing a programming interface to expose. Traditional monolithic kernel designers choose a high-level portable interface that necessarily hides many hardware details. On the other extreme, designs such as the Exokernel optimize for performance and low-level hardware access. Abstraction choices have further implications in how easily and quickly new hardware and application models can be supported. In this paper, we describe the null-Kernel, a new model for structuring system software that attempts to relieve OS designers of this choice and enable access to and composition of OS interfaces at different levels of abstraction. The null-Kernel is designed to address the growing need to easily provide high-level programming interfaces to new Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. HotOS ’19, May 13–15, 2019, Bertinoro, Italy © 2019 Copyright held by the owner/author(s). Publication rights licensed

Bitcoin relies on a peer-to-peer overlay network to broadcast transactions and blocks. From the viewpoint of network measurement, we would like to observe this topology so we can characterize its performance, fairness and robustness. However, this is difficult because Bitcoin is deliberately designed to hide its topology from onlookers. Knowledge of the topology is not in itself a vulnerability, although it could conceivably help an attacker performing targeted eclipse attacks or to deanonymize transaction senders. In this paper we present TxProbe, a novel technique for reconstructing the Bitcoin network topology. TxProbe makes use of peculiarities in how Bitcoin processes out of order, or "orphaned" transactions. We conducted experiments on Bitcoin testnet that suggest our technique reconstructs topology with precision and recall surpassing 90%. We also used TxProbe to take a snapshot of the Bitcoin testnet in just a few hours. TxProbe may be useful for future measurement campaigns of Bitcoin or other cryptocurrency networks.

Modern neural networks often require deep compositions of high-dimensional nonlinear functions (wide architecture) to achieve high test accuracy, and thus can have overwhelming number of parameters. Repeated high cost in prediction at test-time makes neural networks ill-suited for devices with constrained memory or computational power. We introduce an efficient mechanism, reshaped tensor decomposition, to compress neural networks by exploiting three types of invariant structures: periodicity, modulation and low rank. Our reshaped tensor decomposition method exploits such invariance structures using a technique called tensorization (reshaping the layers into higher-order tensors) combined with higher order tensor decompositions on top of the tensorized layers. Our compression method improves low rank approximation methods and can be incorporated to (is complementary to) most of the existing compression methods for neural networks to achieve better compression. Experiments on LeNet-5 (MNIST), ResNet-32 (CI- FAR10) and ResNet-50 (ImageNet) demonstrate that our reshaped tensor decomposition outperforms (5% test accuracy improvement universally on CIFAR10) the state-of-the-art low-rank approximation techniques under same compression rate, besides achieving orders of magnitude faster convergence rates.

New applications enabled by personal smart devices and the Internet-of-Things (IoT) require communication in the context of periods of spatial co-location. Examples of this encounter-based communication (EbC) include social exchange among individuals who shared an experience, and interaction among personal and IoT devices that provide location-based services. Existing EbC systems are limited to communication among participants that share a direct encounter. This paper is inspired by two insights: (1) encounters also enable group communication among devices connected by paths in the encounter graph that is contextual, spontaneous, secure, and does not require users to reveal identifying or linkable information; and (2) addressing communication partners using encounter closures subject to causal, spatial, and temporal constraints enables powerful new forms of group communication. We present the design of enClosure, a service providing group communication based on encounter closures for mobile and IoT applications, and a prototype implementation for Android and the Microsoft Embedded Social Cloud platform. Using real-world traces, we show that enClosure provides a privacy-preserving, secure platform for a wide range of group communication applications ranging from connecting attendees of a large event and virtual guest books to disseminating health risk warnings, lost-and-found, and tracing missing persons.

Modern neural networks can have tens of millions of parameters, and are often ill-suited for smartphones or IoT devices. In this paper, we describe an efficient mechanism for compressing large networks by {em tensorizing/} network layers: i.e. mapping layers on to high-order matrices, for which we introduce new tensor decomposition methods. Compared to previous compression methods, some of which use tensor decomposition, our techniques preserve more of the networks invariance structure. Coupled with a new data reconstruction-based learning method, we show that tensorized compression outperforms existing techniques for both convolutional and fully-connected layers on state-of-the art networks.

Reliable on-off control of peripherals on smart devices is a key to security and privacy in many scenarios. Journalists want to reliably turn off radios to protect their sources during investigative reporting. Users wish to ensure cameras and microphones are reliably off during private meetings. In this paper, we present SeCloak, an ARM TrustZone-based solution that ensures reliable on-off control of peripherals even when the platform software is compromised. We design a secure kernel that co-exists with software running on mobile devices (e.g., Android and Linux) without requiring any code modifications. An Android prototype demonstrates that mobile peripherals like radios, cameras, and microphones can be controlled reliably with a very small trusted computing base and with minimal performance overhead.

We present Sonoloc, a mobile app and system that allows a set of co-located commodity smart devices to determine their relative positions without local infrastructure. Sonoloc enables users to address each other based on their relative positions at events like meetings, talks, or conferences. This capability can, for instance, aid spontaneous communication among users based on their relative position (e.g., in a given section of a room, at the same table, or in a given seat), facilitate interaction between speaker and audience in a lecture hall, and enable the distribution of materials, crowdsensing, and feedback collection based on users' location. Sonoloc can position any number of devices within acoustic range with a constant number of chirps emitted by a self-organized subset of devices. Our experimental evaluation shows that the system can locate up to hundreds of devices with an accuracy of tens of centimeters using up to 15 audio chirps emitted by dynamically selected devices, in actual rooms and despite substantial background noise.

Reliable on-off control of peripherals on smart devices is a key to security and privacy in many scenarios. Journalists want to reliably turn off radios to protect their sources during investigative reporting. Users wish to ensure cameras and microphones are reliably off during private meetings. In this paper, we present SeCloak, an ARM TrustZone-based solution that ensures reliable on-off control of peripherals even when the platform software is compromised. We design a secure kernel that co-exists with software running on mobile devices (e.g., Android and Linux) without requiring any code modifications. An Android prototype demonstrates that mobile peripherals like radios, cameras, and microphones can be controlled reliably with a very small trusted computing base and with minimal performance overhead.

We propose tensorial neural networks (TNNs), a generalization of existing neural networks by extending tensor operations on low order operands to those on high order ones. The problem of parameter learning is challenging, as it corresponds to hierarchical nonlinear tensor decomposition. We propose to solve the learning problem using stochastic gradient descent by deriving nontrivial backpropagation rules in generalized tensor algebra we introduce. Our proposed TNNs has three advantages over existing neural networks: (1) TNNs naturally apply to high order input object and thus preserve the multi-dimensional structure in the input, as there is no need to flatten the data. (2) TNNs interpret designs of existing neural network architectures. (3) Mapping a neural network to TNNs with the same expressive power results in a TNN of fewer parameters. TNN based compression of neural network improves existing low-rank approximation based compression methods as TNNs exploit two other types of invariant structures, periodicity and modulation, in addition to the low rankness. Experiments on LeNet-5 (MNIST), ResNet-32 (CIFAR10) and ResNet-50 (ImageNet) demonstrate that our TNN based compression outperforms (5% test accuracy improvement universally on CIFAR10) the state-of-the-art low-rank approximation based compression methods under the same compression rate, besides achieving orders of magnitude faster convergence rates due to the efficiency of TNNs.

Internet anycast depends on inter-domain routing to direct clients to their "closest" sites. Using data collected from a root DNS server for over a year (400M+ queries/day from 100+ sites), we characterize the load balancing and latency performance of global anycast. Our analysis shows that site loads are often unbalanced, and that most queries travel longer than necessary, many by over 5000 km. Investigating the root causes of these inefficiencies, we can attribute path inflation to two causes. Like unicast, anycast routes are subject to inter-domain routing topology and policies that can increase path length compared to theoretical shortest (e.g., great-circle distance). Unlike unicast, anycast routes are also affected by poor route selection when paths to multiple sites are available, subjecting anycast routes to an additional, unnecessary, penalty. Unfortunately, BGP provides no information about the number or goodness of reachable anycast sites. We propose an additional hint in BGP advertisements for anycast routes that can enable ISPs to make better choices when multiple "equally good" routes are available. Our results show that use of such routing hints can eliminate much of the anycast path inflation, enabling anycast to approach the performance of unicast routing.

Anycast is widely used in critical Internet infrastructures, including root DNS servers, to improve their scalability, resilience, and geographic proximity to clients. In practice, anycast depends on interdomain routing to direct clients to their “closest” sites. As a result, anycast’s performance is largely a result of available BGP routes. We provide what we believe to be the first longitudinal study of how anycast performs in providing load balancing and geographic proximity. We examine about 400M queries per day collected from over 100 anycast sites of the D root DNS server for over a year. From this data, we find evidence of excessively unbalanced load: several anycast sites absorb the majority of the total traffic. Moreover, we find that most of the clients do not use geographically proximal sites; queries on average travel twice the minimum distance. To investigate the root cause of these inefficiencies, we use more than 9,000 probes in RIPE Atlas to measure 9 out of 13 DNS root servers. We show two main causes for poorly balanced load and long query distance: insufficient peering between the hosting domain and large ISPs, and misconfigured routes, often due to route leakages, from ISPs with poor

MOTIVATION:Developing targeted therapeutics and identifying biomarkers relies on large amounts of research participant data. Beyond human DNA, scientists now investigate the DNA of micro-organisms inhabiting the human body. Recent work shows that an individual's collection of microbial DNA consistently identifies that person and could be used to link a real-world identity to a sensitive attribute in a research dataset. Unfortunately, the current suite of DNA-specific privacy-preserving analysis tools does not meet the requirements for microbiome sequencing studies. RESULTS:To address privacy concerns around microbiome sequencing, we implement metagenomic analyses using secure computation. Our implementation allows comparative analysis over combined data without revealing the feature counts for any individual sample. We focus on three analyses and perform an evaluation on datasets currently used by the microbiome research community. We use our implementation to simulate sharing data between four policy-domains. Additionally, we describe an application of our implementation for patients to combine data that allows drug developers to query against and compensate patients for the analysis. AVAILABILITY AND IMPLEMENTATION:The software is freely available for download at: http://cbcb.umd.edu/∼hcorrada/projects/secureseq.html SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online. CONTACT:hcorrada@umiacs.umd.edu.

The ubiquity of portable mobile devices equipped with built-in cameras have led to a transformation in how and when digital images are captured, shared, and archived. Photographs and videos from social gatherings, public events, and even crime scenes are commonplace online. While the spontaneity afforded by these devices have led to new personal and creative outlets, privacy concerns of bystanders (and indeed, in some cases, unwilling subjects) have remained largely unaddressed. We present I-Pic, a trusted software platform that integrates digital capture with user-defined privacy. In I-Pic, users choose alevel of privacy (e.g., image capture allowed or not) based upon social context (e.g., out in public vs. with friends vs. at workplace). Privacy choices of nearby users are advertised via short-range radio, and I-Pic-compliant capture platforms generate edited media to conform to privacy choices of image subjects. I-Pic uses secure multiparty computation to ensure that users' visual features and privacy choices are not revealed publicly, regardless of whether they are the subjects of an image capture. Just as importantly, I-Pic preserves the ease-of-use and spontaneous nature of capture and sharing between trusted users. Our evaluation of I-Pic shows that a practical, energy-efficient system that conforms to the privacy choices of many users within a scene can be built and deployed using current hardware.