Active Adversary Research Articles

A Novel Authenticated Quantum Anonymous Secret Sharing for Classical and Quantum Information

AbstractAnonymous secret sharing (ASS) is an essential cryptographic concept that facilitates the sharing and reconstruction of secret information while safeguarding the identity of the involved secret receivers, which has broad applications in key management, data backup, and distributed systems. In this study, a novel authenticated quantum anonymous secret sharing (QASS) protocol that emphasizes information privacy and identity anonymity protection is proposed. Employing ‐level multipartite GHZ states as a quantum resource, one‐sided anonymous entanglement (AE) is innovatively established between the dealer and anonymous receivers, enabling the dealer to distribute a random share of secret information. Additionally, by establishing a one‐sided AE between anonymous receivers and restorer, the restorer can securely collect and reconstruct the secret information using quantum teleportation (QT). Rigorous security analysis demonstrates that protocol can resist attacks from active adversaries and potentially dishonest users. Quantum experiments on IBM Qiskit validate the correctness and feasibility of the proposed QASS protocol. This work contributes to the advancement of quantum anonymous communication, addressing the requirements for information privacy and identity anonymity in practical application environments.

Adversarial attacks on reinforcement learning agents for command and control

Given the recent impact of deep reinforcement learning in training agents to win complex games such as StarCraft and DoTA (Defense Of The Ancients)—there has been a surge in research for exploiting learning-based techniques for professional wargaming, battlefield simulation, and modeling. Real-time strategy games and simulators have become a valuable resource for operational planning and military research. However, recent work has shown that such learning-based approaches are highly susceptible to adversarial perturbations. In this paper, we investigate the robustness of an agent trained for a command and control task in an environment that is controlled by an active adversary. The C2 agent is trained on custom StarCraft II maps using the state-of-the-art RL algorithms—Asynchronous Advantage Actor Critic (A3C) and proximal policy optimization (PPO). We empirically show that an agent trained using these algorithms is highly susceptible to noise injected by the adversary and investigate the effects these perturbations have on the performance of the trained agent. Our work highlights the urgent need to develop more robust training algorithms especially for critical arenas like the battlefield.

TOPAS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {TOPAS}$$\\end{document}2-pass key exchange with full perfect forward secrecy and optimal communication complexity

We present Transmission optimal protocol with active security (TOPAS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {TOPAS}$$\\end{document}), the first key agreement protocol with optimal communication complexity (message size and number of rounds) that provides security against fully active adversaries. The size of the protocol messages and the computational costs to generate them are comparable to the basic Diffie-Hellman protocol over elliptic curves (which is well-known to only provide security against passive adversaries). Session keys are indistinguishable from random keys—even under reflection and key compromise impersonation attacks. What makes TOPAS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {TOPAS}$$\\end{document}stand out is that it also features a security proof of full perfect forward secrecy (PFS), where the attacker can actively modify messages sent to or from the test-session. The proof of full PFS relies on two new extraction-based security assumptions. It is well-known that existing implicitly-authenticated 2-message protocols like HMQV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {HMQV}$$\\end{document}cannot achieve this strong form of (full) security against active attackers (Krawczyk, Crypto’05). This makes TOPAS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {TOPAS}$$\\end{document}the first key agreement protocol with full security against active attackers that works in prime-order groups while having optimal message size. We also present a variant of our protocol, TOPAS+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {TOPAS+}$$\\end{document}, which, under the Strong Diffie-Hellman assumption, provides better computational efficiency in the key derivation phase. Finally, we present a third protocol termed FACTAS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {FACTAS}$$\\end{document}(for factoring-based protocol with active security) which has the same strong security properties as TOPAS\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {TOPAS}$$\\end{document}and TOPAS+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extsf {TOPAS+}$$\\end{document}but whose security is solely based on the factoring assumption in groups of composite order (except for the proof of full PFS).

Privacy-preserving Crime Reporting and Rewarding in the Presence of Active Insider and Outsider Adversaries

Security agencies collect evidence from whistleblowers to take action against criminals. Moreover, security agencies provide rewards to encourage whistleblowers for their services. However, sometimes, security agencies may have individuals who are not trustworthy. Such insider adversaries disclose the identities of whistleblowers and modify the order of evidence submission to provide rewards to specific whistleblowers. In this paper, we present the crime-reporting and rewarding mechanism to protect the identities of whistleblowers and reward genuine and first whistleblowers. The proposed mechanism not only protects in the presence of outsider adversaries but also in the presence of active insider adversaries. To the best of our knowledge, the proposed mechanism is reliable in identifying and rewarding genuine and first whistleblowers. We use the public ledger to timestamp and record submissions and smart contracts to automatically transfer rewards to genuine and first whistleblowers. To show the feasibility, we implemented the proposed mechanism. Security and performance analysis show that the proposed mechanism is secure against active insider and outsider adversaries, reliable, and transparent.

Two-Stage Optimal Hypotheses Testing for a Model of Stegosystem with an Active Adversary

We study the information-theoretic model of stegosystem with an active adversary, where unlike a passive adversary he can not only read but also write. The legitimate sender as well as the adversary can embed or not a message in the sending data. The receiver’s first task is to decide whether the communication is a covertext, data with no hidden message, or a stegotext, modified data with a hidden secret message. In case of stegotext, the receiver’s second task is to decide whether the message was sent by a legitimate sender or from an adversary. For this purpose an authenticated encryption from the legitimate sender is considered. In this paper we suggest two-stage statistical hypothesis testing approach from the receivers point of view. We propose the logarithmically asymptotically optimal testing for this model. As a result the functional dependence of reliabilities of the first and second kind of errors in both stages is constructed. A comparison of overall error probabilities with the situation of one stage hypotheses testing is discussed and the behaviour of functional dependences of reliabilities are illustrated.

An efficient authentication protocol for smart grid communication based on on-chip-error-correcting physical unclonable function

Security has become a main concern for the smart grid to move from research and development to industry. Central to this security paradigm is the concept of resistance to threats, whether emanating from an active adversary seeking to disrupt operations or a passive entity covertly intercepting sensitive data. In this dynamic landscape, smart meters (SMs) stand as the sentinels at the edge of the grid, silently gathering and transmitting invaluable data. Yet, this very placement often leaves them vulnerable in unprotected areas, underscoring the paramount importance of physical security in the smart grid. It is here that Physical Unclonable Functions (PUFs) have emerged as a formidable ally. These unique constructs serve as guardians of physical security, leveraging the inherent unpredictability of manufacturing processes to create cryptographic keys. PUFs, however, are not without their own conundrums, primarily in the realm of reliability. This challenge has prevented their widespread integration into cryptographic applications. Fuzzy extractors have been considered as a solution to solve the reliability problem of PUFs, albeit at the cost of imposing significant computational burdens. To that end, we first propose an on-chip-error-correcting (OCEC) PUF that efficiently generates stable digits for the authentication process. Afterward, we introduce a lightweight authentication protocol between the SMs and neighborhood gateway (NG) based on the proposed PUF. The provable security analysis shows that not only the proposed protocol can stand secure in the Canetti–Krawczyk (CK) adversary model but also provides additional security features. Also, the performance evaluation demonstrates the significant improvement of the proposed scheme in comparison with the state-of-the-art.

Shielding Graph for eXact Analytics With SGX

Graphs nicely capture data from various domains, allowing the computations of many analytic tasks via graph queries. Graphs of real-world data are often large, albeit useful, and the involved computation can be too heavyweight for commodity computers. For secure outsourcing, we propose (SGX) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> , a forward-secure structured encryption scheme for graph data, which uses lightweight cryptographic techniques with a trusted execution environment such as SGX. To process million-scale graphs by the limited memory of SGX, we load data on-demand using Dijkstra's algorithm and Fibonacci heap. Compared with most prior graph encryption schemes, (SGX) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> supports exact shortest-distance queries instead of approximation and can be easily extended to other graph-based analytics. Finally, we discuss some generic enhancements addressing active adversaries trying to exploit leakages originating from SGX.

Lattice based distributed threshold additive homomorphic encryption with application in federated learning

In federated learning (FL), a parameter server needs to aggregate user gradients and a user needs to protect the value of their gradients. Among all the possible solutions to the problem, those based on additive homomorphic encryption (AHE) are natural. As users may drop out in FL and an adversary could corrupt some users and the parameter server, we require a dropout-resilient AHE scheme with a distributed key generation algorithm. In this paper, we aim to provide a lattice based distributed threshold AHE (DTAHE) scheme and to show their applications in FL. The main merit of the DTAHE scheme is to save communication bandwidth compared with other latticed based DTAHE schemes. Embedding the scheme into FL, we get two secure aggregation protocols. One is secure against a semi-honest adversary and the other is secure against an active adversary. The latter exploits a smart contract in a ledger. Finally, we provide security proofs and performance analysis for the scheme and protocols.

Dap-FL: Federated Learning Flourishes by Adaptive Tuning and Secure Aggregation

Federated learning (FL), an attractive and promising distributed machine learning paradigm, has sparked extensive interest in exploiting tremendous data stored on ubiquitous mobile devices. However, conventional FL suffers severely from resource heterogeneity, as clients with weak computational and communication capabilities may be unable to complete local training using the same local training hyper-parameters. In this paper, we propose Dap-FL, a deep deterministic policy gradient (DDPG)-assisted adaptive FL system, in which local learning rates and local training epochs are adaptively adjusted by all resource-heterogeneous clients through locally deployed DDPG-assisted adaptive hyper-parameter selection schemes. Particularly, the rationality of the proposed hyper-parameter selection scheme is confirmed through rigorous mathematical proof. Besides, due to the thoughtlessness of security consideration of adaptive FL systems in previous studies, we introduce the Paillier cryptosystem to aggregate local models in a secure and privacy-preserving manner. Rigorous analyses show that the proposed Dap-FL system could protect clients' private local models against chosen-plaintext attacks and chosen-message attacks in a widely used honest-but-curious participants and active adversaries security model. More importantly, through ingenious and extensive experiments, the proposed Dap-FL achieves higher model prediction accuracy than two state-of-the-art RL-assisted FL methods, i.e., 6.03% higher than DDPG-based FL and 7.85% higher than DQN-based FL. In addition, experimental results also show that the proposed Dap-FL achieves higher global model prediction accuracy and faster convergence rates than conventional FL, and the comprehensiveness of the adjusted local training hyper-parameters is validated.

Continuous Entity Authentication in the Internet of Things Scenario

In the context of the Internet of Things (IoT), the proliferation of identity spoofing threats has led to the need for the constant entity verification of devices. Recently, a formal framework has been proposed to study resistance to impersonation attacks for One-Message Unilateral Entity Authentication (OM-UEA) schemes, in which the prover continuously authenticates itself through the use of a sequence of authentication messages. Given the limited computing power of the parties (particularly the prover) and the often limited bandwidth channel, in the IoT scenario it is desirable to design unilateral entity authentication schemes that require the use of a single message per session and light computations. In this paper, we first show that OM-UEA schemes can be implemented through digital signatures and that a weak form of unforgeability is sufficient to achieve security against active adversaries. We then apply the signature scheme proposed by Yang et al. in ASIACCS 2020 to our framework, resulting in an OM-UEA scheme that requires minimal computational effort and low storage requirements for the prover. Inspired by this last construction, we propose an OM-UEA scheme based on the hardness of the discrete logarithm problem, which further improves the computational performance for the prover. Our findings offer feasible options for implementing secure continuous entity authentication in IoT applications.

HCALA: Hyperelliptic curve-based anonymous lightweight authentication scheme for Internet of Drones

A drone is often called an ”Unmanned Aerial Vehicle” or UAV. It is utilized in various civilian and military applications, including agriculture, surveillance, and delivery of packages. A promising idea for enhancing the safety and quality of drone flight is to build the Internet of Drones (IoD), in which drones are used to collect sensitive data, which is then communicated in real-time to external user (Ui) through the Ground Station Server (GSS). Before deployment, the GSS and all drones are registered with Control Room (CR), a central authority, which is a trusted authority. To ensure secure and reliable communications, an efficient and secure authentication scheme is needed to enable users and drones to authenticate each other and share a session key. Furthermore, because drones generally have small batteries and limited memory capacity, efficient and lightweight security techniques are suitable for them. Many schemes to secure IoD environments have been proposed recently; however, some were proven as insecure, and some degraded efficiency. In this work, we focus on developing a novel blockchain-based authentication scheme, called HCALA, on protecting the communication between an external user and drone utilizing Hyperelliptic Curve Cryptography (HECC). To evaluate the viability and efficacy of HCALA, We employ the extensively used Random Oracle Model (ROM) and formal security verification using a software tool called AVISPA, which is used to validate the internet security protocols. HCALA is also examined by utilizing informal security analysis techniques, demonstrating that the proposed protocol can withstand several well-known active and passive adversary attacks. It also shows that HCALA is more efficient regarding different parameters, according to the performance comparison. Compared to previous similar schemes, the security and functionality aspects are improved, and the computation, communication costs, and energy consumption are reduced.

Simulation of Elliptical Curve Cryptography in IPSec on Ad-Hoc Networks

Ad-hoc networks have gained significant attention in the realm of communication due to the proliferation of mobile and IoT devices and wireless networks. Ad hoc Networks offer a decentralized approach, where each node can function as a router and a terminal. Ensuring data safety and integrity in Ad hoc Networks remains a challenge, necessitating the use of robust security mechanisms. This research focuses on the simulation of Elliptical Curve Cryptography (ECC) in the IPsec protocol on ad-hoc networks. ECC, known for its strong security and smaller key sizes, provides an effective means of protecting data packets from potential attacks. The Ad hoc On Demand Multipath Distance Vector (AODV) routing protocol is employed for secure data transmission in a Ad hoc Networks. The main objective is to maintain packet security in the face of hostile environments and active adversaries. Furthermore, the results obtained from the NS-2 simulator are compared with Advanced Encryption Standard (AES), and Rivest-Shamir-Adleman (RSA). Evaluation metrics such as Quality of Service (QoS), average processing time, and average end-to-end delay are utilized. This study addresses the challenges faced by ad-hoc networks in an increasingly digital world. By exploring the implementation of ECC-based cryptography, it contributes to the development of secure communication protocols in ad-hoc networks. The findings offer insights into the efficacy of ECC in protecting data transmission and its comparative performance with other cryptographic techniques. Ultimately, this research aims to advance secure communication protocols, ensuring reliable data exchange in diverse applications and scenarios.

Arbitrarily Varying Wiretap Channels With Non-Causal Side Information at the Jammer

Secure communication in a potentially hostile environment is becoming more and more critical. The <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> rbitrarily <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> arying <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</b> iretap <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> hannel (AVWC) provides information-theoretical bounds on how much information can be exchanged even in the presence of an active attacker. If the active attacker has non-causal side information, situations in which a legitimate communication system has been hacked can be modeled. We investigate the AVWC with non-causal side information at the jammer for the case that there exists a best channel to the eavesdropper. Non-causal side information means that the transmitted codeword is known to an active adversary before it is transmitted. By considering the maximum error criterion, we also allow messages to be known at the jammer before the corresponding codeword is transmitted. A single-letter formula for the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> ommon <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</b> andomness (CR)-assisted secrecy capacity is derived. Additionally, we provide a formula for the CR-assisted secrecy capacity for the cases where the channel to the eavesdropper is strongly degraded, strongly noisier, or strongly less capable with respect to the main channel. Furthermore, we compare our results to the CR-assisted secrecy capacity for the cases of maximum error criterion but without non-causal side information at the jammer (blind adversary), maximum error criterion with non-causal side information of the messages at the jammer (semi-blind adversary), and the case of average error criterion without non-causal side information at the jammer (blind adversary).

Toward Designing a Secure Authentication Protocol for IoT Environments

Authentication protocol is a critical part of any application to manage the access control in many applications. A former research recently proposed a lightweight authentication scheme to transmit data in an IoT subsystem securely. Although the designers presented the first security analysis of the proposed protocol, that protocol has not been independently analyzed by third-party researchers, to the best of our knowledge. On the other hand, it is generally agreed that no cryptosystem should be used in a practical application unless its security has been verified through security analysis by third parties extensively, which is addressed in this paper. Although it is an efficient protocol by design compared to other related schemes, our security analysis identifies the non-ideal properties of this protocol. More specifically, we show that this protocol does not provide perfect forward secrecy. In addition, we show that it is vulnerable to an insider attacker, and an active insider adversary can successfully recover the shared keys between the protocol’s entities. In addition, such an adversary can impersonate the remote server to the user and vice versa. Next, the adversary can trace the target user using the extracted information. Finally, we redesign the protocol such that the enhanced protocol can withstand all the aforementioned attacks. The overhead of the proposed protocol compared to its predecessor is only 15.5% in terms of computational cost.

Authenticated Key Exchange Protocol in the Standard Model under Weaker Assumptions

A two-party authenticated key exchange (AKE) protocol allows each of the two parties to share a common secret key over insecure channels, even in the presence of active adversaries who can actively control and modify the exchanged messages. To capture the malicious behaviors of the adversaries, there have been many efforts to define security models. Amongst them, the extended Canetti–Krawczyk (eCK) security model is considered one of the strongest security models and has been widely adopted. In this paper, we present a simple construction of a pairing-based eCK-secure AKE protocol in the standard model. Our protocol can be instantiated with a suitable signature scheme (i.e., an existentially unforgeable signature scheme against adaptive chosen message attacks). The underlying assumptions of our construction are the decisional bilinear Diffie–Hellman assumption and the existence of a pseudorandom function. Note that the previous eCK-secure protocol constructions either relied on random oracles for their security or used somewhat strong assumptions, such as the existence of strong-pseudorandom functions, target collision-resistant functions, etc., while our protocol construction uses fewer and more-standard assumptions in the standard model. Furthermore, preserving the same security argument, our protocol can be instantiated with any appropriate signature scheme that comes in the future with better efficiency.

Efficient Dropout-Resilient Aggregation for Privacy-Preserving Machine Learning

With the increasing adoption of data-hungry machine learning algorithms, personal data privacy has emerged as one of the key concerns that could hinder the success of digital transformation. As such, Privacy-Preserving Machine Learning (PPML) has received much attention from both academia and industry. However, organizations are faced with the dilemma that, on the one hand, they are encouraged to share data to enhance ML performance, but on the other hand, they could potentially be breaching the relevant data privacy regulations. Practical PPML typically allows multiple participants to individually train their ML models, which are then aggregated to construct a global model in a privacy-preserving manner, e.g., based on multi-party computation or homomorphic encryption. Nevertheless, in most important applications of large-scale PPML, e.g., by aggregating clients' gradients to update a global model for federated learning, such as consumer behavior modeling of mobile application services, some participants are inevitably resource-constrained mobile devices, which may drop out of the PPML system due to their mobility nature. Therefore, the resilience of privacy-preserving aggregation has become an important problem to be tackled. In this paper, we propose a scalable privacy-preserving aggregation scheme that can tolerate dropout by participants at any time, and is secure against both semi-honest and active malicious adversaries by setting proper system parameters. By replacing communication-intensive building blocks with a seed homomorphic pseudo-random generator, and relying on the additive homomorphic property of Shamir secret sharing scheme, our scheme outperforms state-of-the-art schemes by up to 6.37$\times$ in runtime and provides a stronger dropout-resilience. The simplicity of our scheme makes it attractive both for implementation and for further improvements.

Организация конфиденциальных запросов к облаку

The article examines the well-known cryptographic problem of obtaining data from a database by a client so that no one with access to the server except the client himself could obtain information about this request. This problem known as PIR (Private Information Retrieval) was formulated in 1995 by Chor, Goldreich, Kushilevitz and Sudan in the information-theoretic setting. A model of cloud computing is proposed. It includes a cloud, an authentication center, a user, clients, trusted dealer, an active adversary executing the protocol in the cloud. The attacking side has the ability to create fake clients to generate an unlimited number of requests. An algorithm for the organization and database distribution on the cloud and an algorithm for obtaining the required bit were proposed. An injective transformation of bit numbers represented in the l-ary number system by words of length d into words without repeating digits of the same length with an alphabet of 𝒍̂ digits is used, i.e. a transformation {𝟎, ..., 𝒍 − 𝟏}^𝒅 → {𝟎, ..., 𝒍̂ − 𝟏}^𝒅 was constructed. This transformation reduces the probability of disclosure of the requested bit number. The communication complexity and probability of revealing required bit were estimated, taking into account the performed transformation.

Counterfactual Anonymous Quantum Teleportation in the Presence of Adversarial Attacks and Channel Noise.

Hiding the identity of involved participants in the network, known as anonymity, is a crucial issue in some cryptographic applications such as electronic voting systems, auctions, digital signatures, and Byzantine agreements. This paper proposes a new anonymous quantum teleportation protocol based on counterfactual communication where no information-carrying particles pass through the channel. It is achieved by the distribution of a counterfactual entanglement among the participants in the network followed by the establishment of an anonymous entanglement between the sender and the receiver. Afterwards, the sender can anonymously teleport a quantum state to the receiver by utilizing the anonymous entanglement. However, the practicality of the anonymous quantum network mainly calls for two performance measures—robustness against adversarial attacks and noisy environments. Motivated by these demands, firstly, we prove the security of our proposed protocol and show that it achieves both the sender and receiver’s anonymity in the presence of active adversaries and untrusted parties. Along with anonymity, we also ensure the correctness of the protocol and the privacy of the teleported qubit. Finally, we analyze the robustness of our proposed protocol under the presence of channel noise and compare its fidelity with those of the conventional protocols. The main advantage of our proposed protocol is that it can provide useful anonymous quantum resources for teleportation under noisy environment with a higher security compared to previous protocols.

Privacy Amplification With Tamperable Memory via Non-Malleable Two-Source Extractors

We extend the classical problem of privacy amplification to a setting where the active adversary, Eve, is also allowed to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fully corrupt</i> the internal memory (which includes the shared randomness, and local randomness tape) of one of the honest parties, Alice and Bob, before the execution of the protocol. We require that either one of Alice or Bob detects tampering, or they agree on a shared key that is indistinguishable from the uniform distribution to Eve. We obtain the following results: 1) we give a privacy amplification protocol via low-error non-malleable two-source extractors with one source having low min-entropy. In particular, this implies the existence of such (non-efficient) protocols; 2) we show that even slight improvements to the state-of-the-art explicit non-malleable two-source extractors would lead to explicit low-error, low min-entropy two-source extractors, thereby resolving a long-standing open question. This suggests that obtaining (information-theoretically secure) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">explicit</i> non-malleable two-source extractors for (1) might be hard; 3) we present explicit constructions of low-error, low min-entropy non-malleable two-source extractors in the CRS model of (Garg, Kalai, Khurana, Eurocrypt 2020), assuming either the quasi-polynomial hardness of DDH or the existence of nearly-optimal collision-resistant hash functions; 4) we instantiate our privacy amplification protocol with the above mentioned non-malleable two-source extractors in the CRS model, leading to explicit, computationally-secure protocols. This is not immediate from (1) because in the computational setting we need to make sure that, in particular, all randomness sources remain samplable throughout the proof. This requires upgrading the assumption of quasi-polynomial hardness of DDH to sub-exponential hardness of DDH.We emphasize that each of the first three results can be read independently.

Accountable Private Set Cardinality for Distributed Measurement

We introduce cryptographic protocols for securely and efficiently computing the cardinality of set union and set intersection. Our private set-cardinality protocols ( PSC ) are designed for the setting in which a large set of parties in a distributed system makes observations, and a small set of parties with more resources and higher reliability aggregates the observations. PSC allows for secure and useful statistics gathering in privacy-preserving distributed systems. For example, it allows operators of anonymity networks such as Tor to securely answer the questions: How many unique users are using the network? and How many hidden services are being accessed? We prove the correctness and security of PSC in the Universal Composability framework against an active adversary that compromises all but one of the aggregating parties. Although successful output cannot be guaranteed in this setting, PSC either succeeds or terminates with an abort, and we furthermore make the adversary accountable for causing an abort by blaming at least one malicious party. We also show that PSC prevents adaptive corruption of the data parties from revealing past observations, which prevents them from being victims of targeted compromise, and we ensure safe measurements by making outputs differentially private. We present a proof-of-concept implementation of PSC and use it to demonstrate that PSC operates with low computational overhead and reasonable bandwidth. It can count tens of thousands of unique observations from tens to hundreds of data-collecting parties while completing within hours. PSC is thus suitable for daily measurements in a distributed system.