Academic researcher in anonymous communications, privacy-preserving systems, and applied cryptography.

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publications

LARMix: Latency-Aware Routing in Mix Networks

Published in Network and Distributed System Security (NDSS) Symposium, 2024

Anonymous communication systems such as mix networks achieve anonymity at the expense of latency that is introduced to alter the flow of packets and hinder their tracing. A high latency however has a negative impact on usability. In this work, we propose LARMix, a novel latency-aware routing scheme for mixnets that reduces propagation latency with a limited impact on anonymity. LARMix can achieve this while also load balancing the traffic in the network. We additionally show how a network can be configured to maximize anonymity while meeting an average end-to-end latency constraint. Lastly, we perform a security analysis studying various adversarial strategies and conclude that LARMix does not significantly increase adversarial advantage as long as the adversary is not able to selectively compromise mixnodes after the LARMix routing policy has been computed.

Citation: M. Rahimi, P. Kumar, and C. Díaz, "LARMix: Latency-Aware Routing in Mix Networks," in NDSS 2024: 31st Symposium on Network and Distributed System Security, Internet Society.
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CLAM: Client-Aware Routing in Mix Networks

Published in ACM Workshop on Information Hiding and Multimedia Security, 2024

Mix networks (mixnets) enhance anonymity at the cost of increased end-to-end latency, deterring clients from adopting mixnets for web browsing or instant messaging. This often leads clients to seek alternative anonymous communication systems, potentially compromising on anonymity levels. Addressing this, LARMix (NDSS 2024) introduced a strategic message routing aimed at minimizing link latency within mixnets. However, LARMix proposal does not cover reducing link latency from clients to the mixnet. Filling this gap, CLAM presents innovative methodologies for efficient message forwarding from clients to mixnets. Our analysis reveals that clients using CLAM can reduce link latency to the mixnet by up to 90% without significantly burdening the network. Moreover, our results indicate that optimizing client routing in mixnets does not substantially increase the risk of message deanonymization, even with adversaries compromising up to 20% of nodes in the mixnet.

Citation: M. Rahimi, "CLAM: Client-Aware Routing in Mix Networks." In Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security, pp. 199-209. 2024.
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LARMix++: Latency-Aware Routing in Mix Networks with Free Routes Topology

Published in International Conference on Cryptology and Network Security (CANS), 2024

Mix networks (mixnets) enhance anonymity by routing client messages through multiple hops, intentionally delaying or reordering these messages to ensure unlinkability. However, this process increases end-to-end latency, potentially degrading the client experience. To address this issue, LARMix (NDSS, 2024) proposed a low-latency routing methodology specifically designed for stratified mixnet architectures. Our paper extends this concept to Free Routes mixnet designs, where, unlike stratified topologies, there are no restrictions on node connections. We adapt several state-of-the-art low-latency routing strategies from both mix and Tor networks to optimize the Free Routes topology. Despite the benefits, low-latency routing can cause certain mixnodes to receive disproportionate amounts of traffic. To overcome this challenge, we introduce a novel load-balancing algorithm that evenly distributes traffic among nodes without significantly compromising low-latency characteristics. Our analytical and simulation experiments demonstrate a considerable reduction in latency compared to uniform routing methods, with negligible loss in message anonymity, defined as the confusion an adversary experiences when correlating messages exiting the mixnet to an initially targeted input message. Additionally, we provide an analysis of adversarial strategies, revealing a balanced trade-off between low latency and adversary advantages.

Citation: M. Rahimi, "LARMix++: Latency-Aware Routing in Mix Networks with Free Routes Topology." In International Conference on Cryptology and Network Security, pp. 187-211. Singapore: Springer Nature Singapore, 2024.
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MALARIA: Management of Low-Latency Routing Impact on Mix Network Anonymity

Published in International Symposium on Network Computing and Applications (NCA), 2024

Mix networks (mixnets) offer robust anonymity even against adversaries monitoring all network links; however, they impose high latency on communications. To address this, recent research has explored strategic low-latency routing within mixnets. While these strategies appear to reduce latency, their impact on mixnet anonymity has not been carefully assessed, raising concerns about potential deanonymization of clients. Tackling this challenge, this paper first quantifies the anonymity loss associated with low-latency routing techniques in mixnets. Building on these insights, second, we introduce a novel lowlatency routing method that maintains mixnet anonymity while achieving significant latency reductions compared to the state-of-the-art solution LARMix (NDSS, 2024). Our approach also ensures a more balanced load distribution among mixnet nodes. Moreover, under adversarial conditions where parts of the mixnet are compromised, our method does not confer significant advantages to the adversary, unlike LARMix. Thus, our proposal emerges as the optimal choice for low-latency routing in mixnets.

Citation: M. Rahimi, "MALARIA: Management of Low-Latency Routing Impact on Mix Network Anonymity." In 2024 22nd International Symposium on Network Computing and Applications (NCA), pp. 193-202. IEEE, 2024.
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LAMP: Lightweight Approaches for Latency Minimization in Mixnets with Practical Deployment Considerations

Published in Network and Distributed System Security (NDSS) Symposium, 2025

Mixnets are a type of anonymous communication system designed to provide network privacy to users. They route client messages through multiple hops, with each hop (mix) perturbing the traffic patterns, thus making message tracing difficult for a network adversary. However, privacy in mixnets comes at the cost of increased latency, limiting the applications that are usable when accessed through a mixnet. In this work we present LAMP, a set of routing approaches tailored for minimizing the propagation latency in mixnets with minimal impact on anonymity. The design of these approaches is grounded in practical deployment considerations making them lightweight, easy to integrate with existing deployed mixnets and computationally realistic. We evaluate the proposed approaches using latency data from the deployed Nym mixnet and demonstrate that LAMP can reduce latency by a factor of 7.5 (from 153.4ms to 20ms) while maintaining high anonymity. LAMP even outperforms the state-of-the-art system LARMix, providing 3× better latency-anonymity tradeoffs and significantly reducing the computational overhead by ≈ 13900× in comparison to LARMix.

Citation: M. Rahimi, P. Kumar, and C. Díaz, "LAMP: Lightweight Approaches for Latency Minimization in Mixnets with Practical Deployment Considerations," in NDSS 2025: 32nd Symposium on Network and Distributed System Security, Internet Society.
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MOCHA: Mixnet Optimization Considering Honest Client Anonymity

Published in ACM Workshop on Information Hiding and Multimedia Security, 2025

Mix networks (mixnets) safeguard client anonymity by forwarding traffic through multiple intermediary nodes (mixnodes), which reorder and delay messages to obscure communication patterns against a global passive adversary capable of monitoring all network transmissions. The anonymity provided by mixnets is usually assessed with a discrete-event simulator, gauging a target message’s indistinguishability among output messages. While useful for comparative analysis, this approach only approximates the mixnet’s anonymity potential. Hence, this paper sheds light on the necessity of considering the client (originator of messages) itself to gauge anonymity accurately. We further provide an algorithm (simulator) to simulate client anonymity for Loopix mixnets. We conduct experiments to optimize general Loopix mixnet parameters, considering both message and client anonymity. Our findings indicate that message anonymity often provides an upper bound and can yield misleading results for mixnet optimization, underscoring the importance of client anonymity. Additionally, we explore scenarios where client anonymity is significantly compromised due to an insufficient number of clients. To address these cases, we propose a multimixing strategy that enhances client anonymity by effectively merging varied traffic types with different mixing characteristics.

Citation: M. Rahimi, "MOCHA: Mixnet Optimization Considering Honest Client Anonymity." In Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, pp. 98-107. 2025.
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Pre-constructed publicly verifiable secret sharing and applications

Published in Applied Cryptography and Network Security (ACNS), 2025

Our work provides a flexible, efficient PPVSS framework that expands the practical capabilities of secret sharing schemes, setting the stage for future advancements in publicly verifiable threshold protocols and their applications in secure and decentralized systems.

Citation: K. Baghery, N. Knapen, G. Nicolas, and M. Rahimi. "Pre-constructed publicly verifiable secret sharing and applications." In International Conference on Applied Cryptography and Network Security, pp. 89-119. Cham: Springer Nature Switzerland, 2025.
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PARSAN-Mix: Packet-Aware Routing and Shuffling with Additional Noise for Latency Optimization in Mix Networks

Published in Applied Cryptography and Network Security (ACNS), 2025

Mix networks (mix-nets) offer strong anonymity by routing client packets through intermediary hops, where they are shuffled with other packets to obscure their origins from a global adversary monitoring all communication exchanges. However, this anonymity is achieved at the expense of increased end-to-end latency, as packets traverse multiple hops (incurring routing delays) and experience additional delays at each hop for shuffling purposes. Consequently, the overall latency for delivering a message—comprising multiple packets—is determined by the packet with the highest combined routing and shuffling delay, which can significantly degrade the client experience, particularly in latency-sensitive applications. To address this issue, our work first derives the theoretical statistics of the total latency experienced by a message, revealing a clear correlation between latency and the number of packets. Second, we propose two approaches to reduce this total latency. First, we present a method to adjust the shuffling delays at each hop, offsetting potential anonymity loss by integrating client-generated noise, backed by differential privacy guarantees. Next, we introduce packet-aware routing techniques, offering two novel methods that prioritize messages with more packets, forwarding them through faster links. However, this may cause certain nodes to be overloaded with disproportionate traffic. To solve this, we third introduce an efficient load-balancing algorithm to redistribute traffic without compromising the packet-aware nature of the routing. Finally, through comprehensive analytical and simulation experiments, we validate our theoretical latency bounds and evaluate the efficacy of our latency management strategies. The results confirm both methods substantially reduce latency with minimal impact on anonymity, while the strategic routing method remains robust against advanced adversarial attacks.

Citation: M. Rahimi, "PARSAN-Mix: Packet-Aware Routing and Shuffling with Additional Noise for Latency Optimization in Mix Networks." In International Conference on Applied Cryptography and Network Security, pp. 159-188. Cham: Springer Nature Switzerland, 2025.
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On Round-Optimal Computational VSS

Published in IACR Communications in Cryptology (CiC), 2025

We propel the state-of-the-art in round-optimal computational VSS schemes, providing more efficient solutions. The efficiency and simplicity of these new round-optimal VSS schemes render them invaluable tools for various classic and post-quantum secure threshold protocols, such as distributed key generation protocols and threshold signature schemes.

Citation: K. Baghery, N. G. Bardeh, S. Khazaei, and M. Rahimi, "On Round-Optimal Computational VSS," in Communications in Cryptology (CiC), 2025.
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DP-Mix: Differentially Private Routing in Mix Networks

Published in Annual Computer Security Applications Conference (ACSAC), 2025

Mixnets, as overlay networks, ensure anonymity for messages by forwarding them through intermediary nodes that obscure their traffic patterns from network-level adversaries. Nonetheless, the selection of intermediaries is traditionally performed uniformly at random, resulting in optimal routes being chosen no more frequently than suboptimal ones. This often causes messages to traverse inefficient paths that degrade performance or weaken security. While there have been proposals to improve route selection in mixnets, they are limited to latency reduction and rely on heuristic strategies that lack formal anonymity guarantees. To bridge this gap, we develop a framework for differentially private routing in mixnets aimed at general-purpose optimization. In this framework, each candidate route is assigned an optimality score, and routes are then selected to favor high-scoring paths while preserving anonymity under a pure differential privacy guarantee. We instantiate this model for optimizing path security, enhancing reliability, and minimizing communication latency. Additionally, we introduce a gradient-based algorithm applied post hoc to the route selection process—without weakening privacy guarantees—to prevent the over-selection of particular nodes, which could otherwise lead to security vulnerabilities or network congestion.

Citation: M. Rahimi, “DP-Mix: Differentially Private Routing in Mix Networks,” to appear in ACSAC 2025: 41st Annual Computer Security Applications Conference, IEEE.
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OptiMix: Scalable and Distributed Approaches for Latency Optimization in Modern Mixnets

Published in Network and Distributed System Security (NDSS) Symposium, 2026

Mixnets provide network-level anonymity, traded off with increased communication latency, which consequently limits their applicability to only latency-tolerant applications, shrinking the anonymity set to clients engaged in such use cases. Addressing this issue requires optimizing latency, as recently explored in LARMix (NDSS’24) and LAMP (NDSS’25) through node arrangement and strategic routing. However, these approaches are tailored to specific mixnet designs, rely on simplified models and trust assumptions, or suffer from limited practical efficiency. In contrast, OptiMix bridges these gaps by introducing a general low-latency mixnet model adaptable to all well-established designs. To this end, we first propose an efficient distributed protocol for arranging nodes in mixnets that achieves low-latency properties while maintaining unbiasability against adversaries. Second, we introduce novel strategic routing schemes that optimize communication latency. Third, we design a load-balancing algorithm that evenly distributes traffic without undermining the latency-optimized characteristics of the routing strategies. Fourth, we conduct extensive evaluations using data from the deployed Nym mixnet, demonstrating substantial latency reductions with minimal anonymity loss across various mixnet designs—achieving up to 4x performance gains over state-of-the-art solutions. Finally, considering that latency reduction incurs either anonymity degradation or increased bandwidth overhead—as stated by the anonymity trilemma—we propose a cover-routing mechanism that enables clients to benefit from low-latency mixnets without compromising anonymity, at the modest cost of generating additional cover traffic.

Citation: M. Rahimi "OptiMix: Scalable and Distributed Approaches for Latency Optimization in Modern Mixnets," in NDSS 2026: 33rd Symposium on Network and Distributed System Security, Internet Society.
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When Mixnets Fail: Evaluating, Quantifying, and Mitigating the Impact of Adversarial Nodes in Mix Networks

Published in Network and Distributed System Security (NDSS) Symposium, 2026

Mix networks (mixnets) provide clients with communication anonymity against strong network adversaries by traversing their packets independently through randomly selected hops (mixnodes), which disrupt packet linkability. Although this approach, implemented in Nym, maximizes obfuscation against network adversaries, it enables an adversary who compromises a subset of mixnodes (10%/5% of nodes) to entirely nullify the anonymity of all clients whose communication volume with their destination exceeds a certain threshold (4MB/30MB). To mitigate such vulnerabilities, this work develops a set of novel path selection techniques that achieve a trade-off between resistance to network adversaries and resilience against compromised mixnodes. Observing that existing anonymity metrics are insufficient to quantify adversarial risk in mixnets, we additionally introduce effective empirical and simulation-based metrics. Through theoretical, empirical, and simulation-based evaluations, we comprehensively assess our proposals, demonstrating that the proposed approaches reduce the vulnerability to compromised nodes by up to 80%, while conferring limited advantage to network adversaries. Our analysis further reveals that state-of-the-art anonymity metrics, in contrast to our proposed metrics, produce misleading results that influenced certain design choices in Nym.

Citation: M. Rahimi "When Mixnets Fail: Evaluating, Quantifying, and Mitigating the Impact of Adversarial Nodes in Mix Networks," in NDSS 2026: 33rd Symposium on Network and Distributed System Security, Internet Society.
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FlowContrast: Few-Shot Multimedia Traffic Representation Learning in LEO Satellite Networks

Published in IEEE International Conference on Multimedia and Expo (ICME), 2026

Although the adoption of encryption protocols is essential for ensuring data privacy, it introduces significant challenges for traffic classification on the Internet, particularly in low Earth orbit (LEO) satellite networks such as Starlink, where fluctuating RTT, burst jitter, and reordering frequently distort flow patterns. Meanwhile, modern Internet traffic is dominated by multimedia applications (video streaming, social media, audio services), whose burst structures and temporal dynamics differ significantly from conventional terrestrial flows. To address encrypted traffic classification, prior deep learning approaches rely on large labeled datasets and are primarily tailored for terrestrial networks, limiting their generalizability under satellite-specific dynamics. To close this gap, we propose FlowContrast, a framework that combines self-supervised contrastive pre-training with few-shot learning, tailored for satellite multimedia traffic. FlowContrast applies domain-aware augmentations directly on raw flows, simulating satellite-induced perturbations before flow-to-image conversion, preserving temporal dependencies and packet-level semantics. Using massive unlabeled traffic, FlowContrast learns transferable representations adapted with minimal labeled samples. We also release SMA40, the first publicly available dataset of Starlink multimedia applications traffic. Experiments on multiple benchmarks show FlowContrast consistently outperforms state-of-the-art methods, achieving superior cross-domain generalization with reduced label dependency. The SMA40 dataset will be released to facilitate future research

Citation: H. Zhang, G. Xiong, G. Gou, Z. Li, W. Xia, Z. Chen, H. Li, and M.Rahimi, "FlowContrast: Few-Shot Multimedia Traffic Representation Learning in LEO Satellite Networks," in ICME 2026: IEEE International Conference on Multimedia and Expo.

talks

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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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