Perfectly Secure Steganography Using Minimum Entropy Coupling

Steganography is the practice of encoding secret information into innocuous content in such a manner that an adversarial third party would not realize that there is hidden meaning. While this problem has classically been studied in security literature, recent advances in generative models have led to a shared interest among security and machine learning researchers in developing scalable steganography techniques. In this work, we show that a steganography procedure is perfectly secure under Cachin (1998)'s information-theoretic model of steganography if and only if it is induced by a coupling. Furthermore, we show that, among perfectly secure procedures, a procedure maximizes information throughput if and only if it is induced by a minimum entropy coupling. These insights yield what are, to the best of our knowledge, the first steganography algorithms to achieve perfect security guarantees for arbitrary covertext distributions. To provide empirical validation, we compare a minimum entropy coupling-based approach to three modern baselines -- arithmetic coding, Meteor, and adaptive dynamic grouping -- using GPT-2, WaveRNN, and Image Transformer as communication channels. We find that the minimum entropy coupling-based approach achieves superior encoding efficiency, despite its stronger security constraints. In aggregate, these results suggest that it may be natural to view information-theoretic steganography through the lens of minimum entropy coupling

Unconditionally secure quantum bit commitment: Revised

Bit commitment is a primitive task of many cryptographic tasks. It has been proved that the unconditionally secure quantum bit commitment is impossible from Mayers-Lo-Chau No-go theorem. A variant of quantum bit commitment requires cheat sensible for both parties. Another results shows that these no-go theorem can be evaded using the non-relativistic transmission or Minkowski causality. Our goal in this paper is to revise unconditionally secure quantum bit commitment. We firstly propose new quantum bit commitments using distributed settings and quantum entanglement which is used to overcome Mayers-Lo-Chau No-go Theorems. Both protocols are perfectly concealing, perfectly binding, and cheating sensible in asymptotic model against entanglement-based attack and splitting attack from quantum networks. These schemes are then extended to commit secret bits against eavesdroppers. We further propose two new applications. One is to commit qubit states. The other is to commit unitary circuits. These new schemes are useful for committing several primitives including sampling model, randomness, and Boolean functions in cryptographic protocols

Fundamental Limitations within the Selected Cryptographic Scenarios and Supra-Quantum Theories

The following submission constitutes a guide and an introduction to a collection of articles submitted as a Ph.D. dissertation at the University of Gda\'nsk. In the dissertation, we study the fundamental limitations within the selected quantum and supra-quantum cryptographic scenarios in the form of upper bounds on the achievable key rates. We investigate various security paradigms, bipartite and multipartite settings, as well as single-shot and asymptotic regimes. Our studies, however, extend beyond the derivations of the upper bounds on the secret key rates in the mentioned scenarios. In particular, we propose a novel type of rerouting attack on the quantum Internet for which we find a countermeasure and benchmark its efficiency. Furthermore, we propose several upper bounds on the performance of quantum (key) repeaters settings. We derive a lower bound on the secret key agreement capacity of a quantum network, which we tighten in an important case of a bidirectional quantum network. The squashed nonlocality derived here as an upper bound on the secret key rate is a novel non-faithful measure of nonlocality. Furthermore, the notion of the non-signaling complete extension arising from the complete extension postulate as a counterpart of purification of a quantum state allows us to study analogies between non-signaling and quantum key distribution scenarios.Comment: PhD Thesis, University of Gda\'nsk, July 202

Differentially-private Multiparty Clustering

In an era marked by the widespread application of Machine Learning (ML) across diverse domains, the necessity of privacy-preserving techniques has become paramount. The Euclidean k-Means problem, a fundamental component of unsupervised learning, brings to light this privacy challenge, especially in federated contexts. Existing Federated approaches utilizing Secure Multiparty Computation (SMPC) or Homomorphic Encryption (HE) techniques, although promising, suffer from substantial overheads and do not offer output privacy. At the same time, differentially private k-Means algorithms fall short in federated settings. Recognizing the critical need for innovative solutions safeguarding privacy, this work pioneers integrating Differential Privacy (DP) into federated k-Means. The key contributions of this dissertation include the novel integration of DP in horizontally-federated k-Means, a lightweight aggregation protocol offering three orders of magnitude speedup over other multiparty approaches, the application of cluster-size constraints in DP k-Means to enhance state-of-the-art utility, and a meticulous examination of various aggregation methods in the protocol. Unlike traditional privacy-preserving approaches, our innovative design results in a faster, more private, and more accurate solution, significantly advancing the state-of-the-art in privacy-preserving machine learning

Receiver Design and Security for Low Power Wireless Communications Systems

This dissertation focuses on two important areas in wireless communications: receiver design and security. In the first part of this dissertation we consider low data rate receiver design for ultra-wideband (UWB), a wideband radio technology that promises to help solve the frequency allocation problem that often inhibits narrowband systems. Reference-based receivers are promising candidates in the UWB regime, because the conventional rake receiver designs suffers from complexity limitations and inaccuracies in channel estimation. Many reference-based systems have arisen as viable solutions for receivers. We unify these systems as well as other systems into the general framework for performance analysis to suggest the optimal system for varying constraints. We improve the performance of frequency-shifted reference (FSR-UWB) for an average power constraint by halving the frequency offset and employing a sample-and-hold approach across the frame period. Also, we introduce a novel peak mitigation technique; tone reservation, for the multi-differential (MD) version of FSR-UWB, to reduce the high peak-to-average power ratio observed as the data carriers increase. The next part of this dissertation is about wireless security which is ubiquitous in modern news. Cryptography is widely use for security but it assumes limited computational abilities of an eavesdropper, is based on the unproven hardness of the underlying primitives, and allows for the message to be recorded and decrypted later. In this dissertation we consider an information-theoretic security approach to guaranteeing everlasting secrecy. We contribute a new secrecy rate pair outage formulation, where an outage event is based on the instantaneous rates of the destination and the eavesdropper being below and above desired thresholds, respectively. In our new secrecy rate pair outage formulation, two new unaccounted outage events emerge: secrecy breach, where the eavesdropper is above the targeted threshold; unreliable, where the destination is unable to successfully decode the message. The former case must be carefully avoided, while for the latter case we can exploit automatic retransmissions (ARQ) while maintaining the eavesdropper intercept probability below the target threshold. We look at both ``simple\u27\u27 receivers and also complex receivers that use a buffer to store previous messages to maximally combine signal-to-noise ratio (SNR). Then we extend these results to the two-hop case where we maximize the end-to-end secure throughput by optimizing the intercept probability at each eavesdropper given a total end-to-end intercept constraint. Lastly, we consider the difficult case in information-theoretic security: the near eavesdropper case, where we contribute an optimal power allocation algorithm that leverages nearby chatter nodes to generate noise to reduce the probability of intercept by the eavesdropper while minimally impeding the source-to-destination communication. As shown in both one-hop and two-hop cases, allowing a slight outage at the destination for cases of when the eavesdropper is above a specific threshold greatly improves secrecy performance

Analytics over Encrypted Traffic and Defenses

Encrypted traffic flows have been known to leak information about their underlying content through statistical properties such as packet lengths and timing. While traffic fingerprinting attacks exploit such information leaks and threaten user privacy by disclosing website visits, videos streamed, and user activity on messaging platforms, they can also be helpful in network management and intelligence services. Most recent and best-performing such attacks are based on deep learning models. In this thesis, we identify multiple limitations in the currently available attacks and defenses against them. First, these deep learning models do not provide any insights into their decision-making process. Second, most attacks that have achieved very high accuracies are still limited by unrealistic assumptions that affect their practicality. For example, most attacks assume a closed world setting and focus on traffic classification after event completion. Finally, current state-of-the-art defenses still incur high overheads to provide reasonable privacy, which limits their applicability in real-world applications. In order to address these limitations, we first propose an inline traffic fingerprinting attack based on variable-length sequence modeling to facilitate real-time analytics. Next, we attempt to understand the inner workings of deep learning-based attacks with the dual goals of further improving attacks and designing efficient defenses against such attacks. Then, based on the observations from this analysis, we propose two novel defenses against traffic fingerprinting attacks that provide privacy under more realistic constraints and at lower bandwidth overheads. Finally, we propose a robust framework for open set classification that targets network traffic with this added advantage of being more suitable for deployment in resource-constrained in-network devices

Homodyne-based quantum random number generator at 2.9 Gbps secure against quantum side-information

Quantum random number generators promise perfectly unpredictable random numbers. A popular approach to quantum random number generation is homodyne measurements of the vacuum state, the ground state of the electro-magnetic field. Here we experimentally implement such a quantum random number generator, and derive a security proof that considers quantum side-information instead of classical side-information only. Based on the assumptions of Gaussianity and stationarity of noise processes, our security analysis furthermore includes correlations between consecutive measurement outcomes due to finite detection bandwidth, as well as analog-to-digital converter imperfections. We characterize our experimental realization by bounding measured parameters of the stochastic model determining the min-entropy of the system’s measurement outcomes, and we demonstrate a real-time generation rate of 2.9 Gbit/s. Our generator follows a trusted, device-dependent, approach. By treating side-information quantum mechanically an important restriction on adversaries is removed, which usually was reserved to semi-device-independent and device-independent schemes

Practice-Oriented Privacy in Cryptography

While formal cryptographic schemes can provide strong privacy guarantees, heuristic schemes that prioritize efficiency over formal rigor are often deployed in practice, which can result in privacy loss. Academic schemes that do receive rigorous attention often lack concrete efficiency or are difficult to implement. This creates tension between practice and research, leading to deployed privacy-preserving systems that are not backed by strong cryptographic guarantees. To address this tension between practice and research, we propose a practice-oriented privacy approach, which focuses on designing systems with formal privacy models that can effectively map to real-world use cases. This approach includes analyzing existing privacy-preserving systems to measure their privacy guarantees and how they are used. Furthermore, it explores solutions in the literature and analyzes gaps in their models to design augmented systems that apply more clearly to practice. We focus on two settings of privacy-preserving payments and communications. First, we introduce BlockSci, a software platform that can be used to perform analyses on the privacy and usage of blockchains. Specifically, we assess the privacy of the Dash cryptocurrency and analyze the velocity of cryptocurrencies, finding that Dash’s PrivateSend may still be vulnerable to clustering attacks and that a significant fraction of transactions on Bitcoin are “self-churn” transactions. Next, we build a technique for reducing bandwidth in mixing cryptocurrencies, which suffer from a practical limitation: the size of the transaction growing linearly with the size of the anonymity set. Our proposed technique efficiently samples cover traffic from a finite and public set of known values, while deriving a compact description of the resulting transaction set. We show how this technique can be integrated with various currencies and different cover sampling distributions. Finally, we look at the problem of establishing secure communication channels without access to a trusted public key infrastructure. We construct a scheme that uses network latency and reverse turing tests to detect the presence of eavesdroppers, prove our construction secure, and implement it on top of an existing communication protocol. This line of work bridges the gap between theoretical cryptographic research and real-world deployments to bring better privacy-preserving schemes to end users

LIPIcs, Volume 251, ITCS 2023, Complete Volume

LIPIcs, Volume 251, ITCS 2023, Complete Volum