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On Ends-to-Ends Encryption: Asynchronous Group Messaging with Strong Security Guarantees
Get PDFIn the past few years secure messaging has become mainstream, with over a billion active users of end-to-end encryption protocols such as Signal. The Signal Protocol provides a strong property called post-compromise security to its users. However, it turns out that many of its implementations provide, without notification, a weaker property for group messaging: an adversary who compromises a single group member can read and inject messages indefinitely.
We show for the first time that post-compromise security can be achieved in realistic, asynchronous group messaging systems. We present a design called Asynchronous Ratcheting Trees (ART), which uses tree-based Diffie-Hellman key exchange to allow a group of users to derive a shared symmetric key even if no two are ever online at the same time. ART scales to groups containing thousands of members, while still providing provable security guarantees. It has seen significant interest from industry, and forms the basis for two draft IETF RFCs and a chartered working group. Our results show that strong security guarantees for group messaging are practically achievable in a modern setting
Investigating System Operators' Perspective on Security Misconfigurations
Get PDFNowadays, security incidents have become a familiar “nuisance,” and they regularly lead to the exposure of private and sensitive data. The root causes for such incidents are rarely complex attacks. Instead, the attacks are straight-forward, and they are enabled by simple misconfigurations, such as authentication not being required, or security updates not being installed. For example, the leak of over 140 million Americans’ private data from Equifax’s systems ranks among most severe misconfigurations in recent history: The underlying vulnerability was long known, and a security patch had been readily available for months, but it was never applied. Ultimately, Equifax blamed an employee for forgetting to update the affected system, highlighting the personal responsibility of that operator.
In this paper, we investigate the operators’ perspective on security misconfigurations to approach the human component of this class of security issues. We focus our analysis on system operators, as although they are the relevant actors managing the affected systems, they have not yet received significant attention by prior research. We follow an inductive approach and apply a multi-step empirical methodology: (i) a qualitative study to understand how to approach the target group and measure the misconfiguration phenomenon, and (ii) a quantitative survey rooted in the qualitative data. We then provide the first analysis of system operators’ perspective on security misconfigurations, and we determine the factors that operators perceive as the root causes. Based on our findings, we provide practical recommendations on how to reduce security misconfigurations’ frequency and impact
When the Crypto in Cryptocurrencies Breaks: Bitcoin Security under Broken Primitives
No full textDigital currencies such as Bitcoin rely on cryptographic primitives to operate. However, past experience shows that cryptographic primitives do not last forever: increased computational power and advanced cryptanalysis cause primitives to break and motivate the development of new ones. It is therefore crucial for maintaining trust in a cryptocurrency to anticipate such breakage. We present the first systematic analysis of the effect of broken primitives on Bitcoin. We analyze the ways in which Bitcoin’s core cryptographic building blocks can break and the subsequent effect on the main Bitcoin security guarantees. Our analysis reveals a wide range of possible effects depending on the primitive and type of breakage, ranging from minor privacy violations to a complete breakdown of the currency. Our results lead to several suggestions for the Bitcoin migration plans and insights for other cryptocurrencies in case of broken or weakened cryptographic primitives
Causal Inference on Event Sequences
Get PDFGiven two discrete valued time series—that is, event sequences—of length n can we tell whether they are causally related? That is, can we tell whether x^n causes y^n, whether y^n causes x^n? Can we do so without having to make assumptions on the distribution of these time series, or about the lag of the causal effect? And, importantly for practical application, can we do so accurately and efficiently? These are exactly the questions we answer in this paper.
We propose a causal inference framework for event sequences based on information theory. We build upon the well-known notion of Granger causality, and define causality in terms of compression. We infer that x^n is likely a cause of y^n if y^n can be (much) better sequentially compressed given the past of both y^n and x^n, than for the other way around. To compress the data we use the notion of sequential normalized maximal likelihood, which means we use minimax optimal codes with respect to a parametric family of distributions. To show this works in practice, we propose CUTE, a linear time method for inferring the causal direction between two event sequences. Empirical evaluation shows that CUTE works well in practice, is much more robust than transfer entropy, and ably reconstructs the ground truth on river flow and spike train data
Generating Realistic Synthetic Population Datasets
Get PDFModern studies of societal phenomena rely on the availability of large datasets capturing attributes and
activities of synthetic, city-level, populations. For instance, in epidemiology, synthetic population datasets are
necessary to study disease propagation and intervention measures before implementation. In social science,
synthetic population datasets are needed to understand how policy decisions might affect preferences and
behaviors of individuals. In public health, synthetic population datasets are necessary to capture diagnostic
and procedural characteristics of patient records without violating confidentialities of individuals. To generate
such datasets over a large set of categorical variables, we propose the use of the maximum entropy principle
to formalize a generative model such that in a statistically well-founded way we can optimally utilize given
prior information about the data, and are unbiased otherwise. An e�cient inference algorithm is designed
to estimate the maximum entropy model, and we demonstrate how our approach is adept at estimating
underlying data distributions. We evaluate this approach against both simulated data and US census datasets,
and demonstrate its feasibility using an epidemic simulation application
New Constructions of Identity-Based and Key-Dependent Message Secure Encryption Schemes
No full textRecently, Döttling and Garg (CRYPTO 2017) showed how to build identity-based encryption (IBE) from a novel primitive termed Chameleon Encryption, which can in turn be realized from simple number theoretic hardness assumptions such as the computational Diffie-Hellman assumption (in groups without pairings) or the factoring assumption. In a follow-up work (TCC 2017), the same authors showed that IBE can also be constructed from a slightly weaker primitive called One-Time Signatures with Encryption (OTSE).
In this work, we show that OTSE can be instantiated from hard learning problems such as the Learning With Errors (LWE) and the Learning Parity with Noise (LPN) problems. This immediately yields the first IBE construction from the LPN problem and a construction based on a weaker LWE assumption compared to previous works.
Finally, we show that the notion of one-time signatures with encryption is also useful for the construction of key-dependent-message (KDM) secure public-key encryption. In particular, our results imply that a KDM-secure public key encryption can be constructed from any KDM-secure secret-key encryption scheme and any public-key encryption scheme
Model Checking Quantitative Hyperproperties
No full textHyperproperties are properties of sets of computation traces. In this paper, we study quantitative hyperproperties, which we define as hyperproperties that express a bound on the number of traces that may appear in a certain relation. For example, quantitative non-interference limits the amount of information about certain secret inputs that is leaked through the observable outputs of a system. Quantitative non-interference thus bounds the number of traces that have the same observable input but different observable output. We study quantitative hyperproperties in the setting of HyperLTL, a temporal logic for hyperproperties. We show that, while quantitative hyperproperties can be expressed in HyperLTL, the running time of the HyperLTL model checking algorithm is, depending on the type of property, exponential or even doubly exponential in the quantitative bound. We improve this complexity with a new model checking algorithm based on model-counting. The new algorithm needs only logarithmic space in the bound and therefore improves, depending on the property, exponentially or even doubly exponentially over the model checking algorithm of HyperLTL. In the worst case, the new algorithm needs polynomial space in the size of the system. Our Max#Sat-based prototype implementation demonstrates, however, that the counting approach is viable on systems with nontrivial quantitative information flow requirements such as a passcode checker
RVHyper: A Runtime Verification Tool for Temporal Hyperproperties
No full textWe present \backslashtext {RVHyper}RVHyper, a runtime verification tool for hyperproperties. Hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other. Specifications are given as formulas in the temporal logic \backslashtext {HyperLTL}HyperLTL, which extends linear-time temporal logic (LTL) with trace quantifiers and trace variables. \backslashtext {RVHyper}RVHyperprocesses execution traces sequentially until a violation of the specification is detected. In this case, a counter example, in the form of a set of traces, is returned. As an example application, we show how \backslashtext {RVHyper}RVHypercan be used to detect spurious dependencies in hardware designs
Disentangled Person Image Generation
No full textGenerating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information. In this work, we aim at generating such images based on a novel, two-stage reconstruction pipeline that learns a disentangled representation of the aforementioned image factors and generates novel person images at the same time. First, a multi-branched reconstruction network is proposed to disentangle and encode the three factors into embedding features, which are then combined to re-compose the input image itself. Second, three corresponding mapping functions are learned in an adversarial manner in order to map Gaussian noise to the learned embedding feature space, for each factor respectively. Using the proposed framework, we can manipulate the foreground, background and pose of the input image, and also sample new embedding features to generate such targeted manipulations, that provide more control over the generation process. Experiments on Market-1501 and Deepfashion datasets show that our model does not only generate realistic person images with new foregrounds, backgrounds and poses, but also manipulates the generated factors and interpolates the in-between states. Another set of experiments on Market-1501 shows that our model can also be beneficial for the person re-identification task