arXiv subjects

John Liagouris

Publications and source records attributed to John Liagouris.

8 recordsLinked to original sources

arXiv2026

CryptDough: A Unified Analytics Engine for Secure Multiparty Computation

We present CryptDough, a unified analytics engine for secure multiparty computation (MPC). CryptDough enables multiple distrusting parties to jointly execute a data analysis pipeline on their private inputs and learn nothing beyond the result (e.g., aggregate statistics). Unlike existing MPC solutions that support a single threat model or workload type, CryptDough provides built-in support for cross-domain analytics (relational, time series, ML inference) under various threat models, all within the same system runtime. CryptDough contributes (i) a hierarchical system design that facilitates modularity and extensibility through progressive lowering of abstractions, and (ii) the concept of virtual vectors that enable users to write single-threaded code across all layers of the software stack, while pushing the complexity of communication, parallelization, and memory management down to the execution engine. We show that CryptDough generalizes the functionality of state-of-the-art MPC systems and remains competitive on the analytics they support, often outperforming them by more than $2\times$.

cs.CR↗

arXiv2026

ORQ: Complex Analytics on Private Data with Strong Security Guarantees

We present ORQ, a system that enables collaborative analysis of large private datasets using cryptographically secure multi-party computation (MPC). ORQ protects data against semi-honest or malicious parties and can efficiently evaluate relational queries with multi-way joins and aggregations that have been considered notoriously expensive under MPC. To do so, ORQ eliminates the quadratic cost of secure joins by leveraging the fact that, in practice, the structure of many real queries allows us to join records and apply the aggregations "on the fly" while keeping the result size bounded. On the system side, ORQ contributes generic oblivious operators, a data-parallel vectorized query engine, a communication layer that amortizes MPC network costs, and a dataflow API for expressing relational analytics -- all built from the ground up. We evaluate ORQ in LAN and WAN deployments on a diverse set of workloads, including complex queries with multiple joins and custom aggregations. When compared to state-of-the-art solutions, ORQ significantly reduces MPC execution times and can process one order of magnitude larger datasets. For our most challenging workload, the full TPC-H benchmark, we report results entirely under MPC with Scale Factor 10 -- a scale that had previously been achieved only with information leakage or the use of trusted third parties.

cs.CR↗

arXiv2022

Secrecy: Secure collaborative analytics on secret-shared data

We present a relational MPC framework for secure collaborative analytics on private data with no information leakage. Our work targets challenging use cases where data owners may not have private resources to participate in the computation, thus, they need to securely outsource the data analysis to untrusted third parties. We define a set of oblivious operators, explain the secure primitives they rely on, and analyze their costs in terms of operations and inter-party communication. We show how these operators can be composed to form end-to-end oblivious queries, and we introduce logical and physical optimizations that dramatically reduce the space and communication requirements during query execution, in some cases from quadratic to linear or from linear to logarithmic with respect to the cardinality of the input. We implement our framework on top of replicated secret sharing in a system called Secrecy and evaluate it using real queries from several MPC application areas. Our experiments demonstrate that the proposed optimizations can result in over 1000x lower execution times compared to baseline approaches, enabling Secrecy to outperform state-of-the-art frameworks and compute MPC queries on millions of input rows with a single thread per party.

cs.DB↗

arXiv2019

Megaphone: Latency-conscious state migration for distributed streaming dataflows

We design and implement Megaphone, a data migration mechanism for stateful distributed dataflow engines with latency objectives. When compared to existing migration mechanisms, Megaphone has the following differentiating characteristics: (i) migrations can be subdivided to a configurable granularity to avoid latency spikes, and (ii) migrations can be prepared ahead of time to avoid runtime coordination. Megaphone is implemented as a library on an unmodified timely dataflow implementation, and provides an operator interface compatible with its existing APIs. We evaluate Megaphone on established benchmarks with varying amounts of state and observe that compared to naïve approaches Megaphone reduces service latencies during reconfiguration by orders of magnitude without significantly increasing steady-state overhead.

cs.DC↗

arXiv2018

DeltaPath: dataflow-based high-performance incremental routing

Routing controllers must react quickly to failures, reconfigurations and workload or policy changes, to ensure service performance and cost-efficient network operation. We propose a general execution model which views routing as an incremental data-parallel computation on a graph-based network model plus a continuous stream of network changes. Our approach supports different routing objectives with only minor re-configuration of its core algorithm, and easily accomodates dynamic user-defined routing policies. Moreover, our prototype demonstrates excellent performance: on Google Jupiter topology it reacts with a median time of 350ms to link failures and serves more than two million path requests per second each with latency under 1ms. This is three orders-of-magnitude faster than the popular ONOS open-source SDN controller.

cs.NI↗

arXiv2016

graphVizdb: A Scalable Platform for Interactive Large Graph Visualization

We present a novel platform for the interactive visualization of very large graphs. The platform enables the user to interact with the visualized graph in a way that is very similar to the exploration of maps at multiple levels. Our approach involves an offline preprocessing phase that builds the layout of the graph by assigning coordinates to its nodes with respect to a Euclidean plane. The respective points are indexed with a spatial data structure, i.e., an R-tree, and stored in a database. Multiple abstraction layers of the graph based on various criteria are also created offline, and they are indexed similarly so that the user can explore the dataset at different levels of granularity, depending on her particular needs. Then, our system translates user operations into simple and very efficient spatial operations (i.e., window queries) in the backend. This technique allows for a fine-grained access to very large graphs with extremely low latency and memory requirements and without compromising the functionality of the tool. Our web-based prototype supports three main operations: (1) interactive navigation, (2) multi-level exploration, and (3) keyword search on the graph metadata.

cs.HC↗

arXiv2015

Towards Scalable Visual Exploration of Very Large RDF Graphs

In this paper, we outline our work on developing a disk-based infrastructure for efficient visualization and graph exploration operations over very large graphs. The proposed platform, called graphVizdb, is based on a novel technique for indexing and storing the graph. Particularly, the graph layout is indexed with a spatial data structure, i.e., an R-tree, and stored in a database. In runtime, user operations are translated into efficient spatial operations (i.e., window queries) in the backend.

cs.HC↗

arXiv2012

Privacy Preservation by Disassociation

In this work, we focus on protection against identity disclosure in the publication of sparse multidimensional data. Existing multidimensional anonymization techniquesa) protect the privacy of users either by altering the set of quasi-identifiers of the original data (e.g., by generalization or suppression) or by adding noise (e.g., using differential privacy) and/or (b) assume a clear distinction between sensitive and non-sensitive information and sever the possible linkage. In many real world applications the above techniques are not applicable. For instance, consider web search query logs. Suppressing or generalizing anonymization methods would remove the most valuable information in the dataset: the original query terms. Additionally, web search query logs contain millions of query terms which cannot be categorized as sensitive or non-sensitive since a term may be sensitive for a user and non-sensitive for another. Motivated by this observation, we propose an anonymization technique termed disassociation that preserves the original terms but hides the fact that two or more different terms appear in the same record. We protect the users' privacy by disassociating record terms that participate in identifying combinations. This way the adversary cannot associate with high probability a record with a rare combination of terms. To the best of our knowledge, our proposal is the first to employ such a technique to provide protection against identity disclosure. We propose an anonymization algorithm based on our approach and evaluate its performance on real and synthetic datasets, comparing it against other state-of-the-art methods based on generalization and differential privacy.

cs.DB↗