Jodes: Efficient Oblivious Join in the Distributed Setting

• URL: http://arxiv.org/abs/2501.09334v1
• Date: Thu, 16 Jan 2025 07:12:14 GMT
• Title: Jodes: Efficient Oblivious Join in the Distributed Setting
• Abstract summary: We present Jodes, an efficient oblivious join algorithm in the distributed setting.<n>Jodes achieves up to a sixfold performance improvement over state-of-the-art join algorithms.
• Score: 13.270794419242144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trusted execution environment (TEE) has provided an isolated and secure environment for building cloud-based analytic systems, but it still suffers from access pattern leakages caused by side-channel attacks. To better secure the data, computation inside TEE enclave should be made oblivious, which introduces significant overhead and severely slows down the computation. A natural way to speed up is to build the analytic system with multiple servers in the distributed setting. However, this setting raises a new security concern -- the volumes of the transmissions among these servers can leak sensitive information to a network adversary. Existing works have designed specialized algorithms to address this concern, but their supports for equi-join, one of the most important but non-trivial database operators, are either inefficient, limited, or under a weak security assumption. In this paper, we present Jodes, an efficient oblivious join algorithm in the distributed setting. Jodes prevents the leakage on both the network and enclave sides, supports a general equi-join operation, and provides a high security level protection that only publicizes the input sizes and the output size. Meanwhile, it achieves both communication cost and computation cost asymptotically superior to existing algorithms. To demonstrate the practicality of Jodes, we conduct experiments in the distributed setting comprising 16 servers. Empirical results show that Jodes achieves up to a sixfold performance improvement over state-of-the-art join algorithms.

Related papers

• TENNOR: Trustworthy Execution for Neural Networks through Obliviousness and Retrievals [11.751398157718013]
  Training neural networks on sensitive data in untrusted cloud environments requires simultaneously achieving computational efficiency and rigorous privacy guarantees.<n>We present TENNOR, a system that resolves this tension by co-designing the neural network training pipeline with doubly oblivious primitives.<n> TENNOR recasts sparse neuron activation as a locality-sensitive hashing (LSH) retrieval problem, reducing secure sparsification to doubly oblivious accesses over an LSH data structure.
  arXiv  Detail & Related papers  (2026-05-08T02:46:24Z)
• Trust-Aware Routing for Distributed Generative AI Inference at the Edge [0.34376560669160394]
  We present G-TRAC, a trust-aware coordination framework for distributed generative inference.<n>G-TRAC integrates algorithmic path selection with system-level protocol design to ensure robust distributed inference.
  arXiv  Detail & Related papers  (2026-03-30T16:07:11Z)
• A Secure and Private Distributed Bayesian Federated Learning Design [56.92336577799572]
  Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server.<n>DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy.<n>We propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration.
  arXiv  Detail & Related papers  (2026-02-23T16:12:02Z)
• MirageNet:A Secure, Efficient, and Scalable On-Device Model Protection in Heterogeneous TEE and GPU System [8.936421130707204]
  Given high model training costs and user experience requirements, balancing model privacy and low runtime overhead is critical.<n>We propose ConvShatter, a novel obfuscation scheme that achieves low latency and high accuracy while preserving model confidentiality and integrity.
  arXiv  Detail & Related papers  (2026-01-20T10:39:09Z)
• CaMeLs Can Use Computers Too: System-level Security for Computer Use Agents [60.98294016925157]
  AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior to steal credentials or cause financial loss.<n>We introduce Single-Shot Planning for CUAs, where a trusted planner generates a complete execution graph with conditional branches before any observation of potentially malicious content.<n>Although this architectural isolation successfully prevents instruction injections, we show that additional measures are needed to prevent Branch Steering attacks.
  arXiv  Detail & Related papers  (2026-01-14T23:06:35Z)
• AmbShield: Enhancing Physical Layer Security with Ambient Backscatter Devices against Eavesdroppers [69.56534335936534]
  AmbShield is an AmBD-assisted PLS scheme that leverages naturally distributed AmBDs to simultaneously strengthen the legitimate channel and degrade eavesdroppers'<n>In AmbShield, AmBDs are exploited as friendly jammers that randomly backscatter to create interference at eavesdroppers, and as passive relays that backscatter the desired signal to enhance the capacity of legitimate devices.
  arXiv  Detail & Related papers  (2026-01-14T20:56:50Z)
• Aerial Secure Collaborative Communications under Eavesdropper Collusion in Low-altitude Economy: A Generative Swarm Intelligent Approach [84.20358039333756]
  We introduce distributed collaborative beamforming (DCB) into AAV swarms and handle the eavesdropper collusion by controlling the corresponding signal distributions. We minimize the two-way known secrecy capacity and maximum sidelobe level to avoid information leakage from the known and unknown eavesdroppers. We propose a novel generative swarm intelligence (GenSI) framework to solve the problem with less overhead.
  arXiv  Detail & Related papers  (2025-03-02T04:02:58Z)
• Privacy preserving layer partitioning for Deep Neural Network models [0.21470800327528838]
  Trusted Execution Environments (TEEs) can introduce significant performance overhead due to additional layers of encryption, decryption, security and integrity checks. We introduce layer partitioning technique and offloading computations to GPU. We conduct experiments to demonstrate the effectiveness of our approach in protecting against input reconstruction attacks developed using trained conditional Generative Adversarial Network(c-GAN)
  arXiv  Detail & Related papers  (2024-04-11T02:39:48Z)
• Privacy-Preserving Distributed Learning for Residential Short-Term Load Forecasting [11.185176107646956]
  Power system load data can inadvertently reveal the daily routines of residential users, posing a risk to their property security. We introduce a Markovian Switching-based distributed training framework, the convergence of which is substantiated through rigorous theoretical analysis. Case studies employing real-world power system load data validate the efficacy of our proposed algorithm.
  arXiv  Detail & Related papers  (2024-02-02T16:39:08Z)
• $\Lambda$-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI [3.363904632882723]
  We introduce $Lambda$-Split, a split computing framework to facilitate computational offloading. In $Lambda$-Split, a generative model, usually a deep neural network (DNN), is partitioned into three sub-models and distributed across the user's local device and a cloud server. This architecture ensures that only the hidden layer outputs are transmitted, thereby preventing the external transmission of privacy-sensitive raw input and output data.
  arXiv  Detail & Related papers  (2023-10-23T07:44:04Z)
• SOCI^+: An Enhanced Toolkit for Secure OutsourcedComputation on Integers [50.608828039206365]
  We propose SOCI+ which significantly improves the performance of SOCI. SOCI+ employs a novel (2, 2)-threshold Paillier cryptosystem with fast encryption and decryption as its cryptographic primitive. Compared with SOCI, our experimental evaluation shows that SOCI+ is up to 5.4 times more efficient in computation and 40% less in communication overhead.
  arXiv  Detail & Related papers  (2023-09-27T05:19:32Z)
• Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones [75.80952211739185]
  Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard. Adopting an edge-fog computational paradigm, we can offload part of the computation to the fog; however, this poses security concerns if the fog node, or the communication link, can not be trusted. We propose a novel distributed edge-fog execution scheme that validates fog computation by redundantly executing a random subnetwork aboard our nano-drone.
  arXiv  Detail & Related papers  (2023-07-04T08:29:41Z)
• Illicit item detection in X-ray images for security applications [7.519872646378835]
  Automated detection of contraband items in X-ray images can significantly increase public safety. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task. This paper proposes a two-fold improvement of such algorithms for the X-ray analysis domain.
  arXiv  Detail & Related papers  (2023-05-03T07:28:05Z)
• LGTBIDS: Layer-wise Graph Theory Based Intrusion Detection System in Beyond 5G [9.63617966257402]
  Intrusion detection signifies a central approach to ensuring the security of the communication network. A Layerwise Graph Theory-Based Intrusion Detection System (LGTBIDS) algorithm is designed to detect the attacked node. Results validate the better performance, low time computations, and low complexity.
  arXiv  Detail & Related papers  (2022-10-06T05:32:03Z)
• Acceleration in Distributed Optimization Under Similarity [72.54787082152278]
  We study distributed (strongly convex) optimization problems over a network of agents, with no centralized nodes. An $varepsilon$-solution is achieved in $tildemathcalrhoObig(sqrtfracbeta/mu (1-)log1/varepsilonbig)$ number of communications steps. This rate matches (up to poly-log factors) for the first time lower complexity communication bounds of distributed gossip-algorithms applied to the class of problems of interest.
  arXiv  Detail & Related papers  (2021-10-24T04:03:00Z)
• Faster Secure Data Mining via Distributed Homomorphic Encryption [108.77460689459247]
  Homomorphic Encryption (HE) is receiving more and more attention recently for its capability to do computations over the encrypted field. We propose a novel general distributed HE-based data mining framework towards one step of solving the scaling problem. We verify the efficiency and effectiveness of our new framework by testing over various data mining algorithms and benchmark data-sets.
  arXiv  Detail & Related papers  (2020-06-17T18:14:30Z)
• Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing [55.012801269326594]
  In Byzantine robust distributed learning, a central server wants to train a machine learning model over data distributed across multiple workers. A fraction of these workers may deviate from the prescribed algorithm and send arbitrary messages. We propose a simple bucketing scheme that adapts existing robust algorithms to heterogeneous datasets at a negligible computational cost.
  arXiv  Detail & Related papers  (2020-06-16T17:58:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.