arXiv subjects

David Basin

Publications and source records attributed to David Basin.

At least 37 records · Page 2Linked to original sources

arXiv2021

The EMV Standard: Break, Fix, Verify

EMV is the international protocol standard for smartcard payment and is used in over 9 billion cards worldwide. Despite the standard's advertised security, various issues have been previously uncovered, deriving from logical flaws that are hard to spot in EMV's lengthy and complex specification, running over 2,000 pages. We formalize a comprehensive symbolic model of EMV in Tamarin, a state-of-the-art protocol verifier. Our model is the first that supports a fine-grained analysis of all relevant security guarantees that EMV is intended to offer. We use our model to automatically identify flaws that lead to two critical attacks: one that defrauds the cardholder and a second that defrauds the merchant. First, criminals can use a victim's Visa contactless card to make payments for amounts that require cardholder verification, without knowledge of the card's PIN. We built a proof-of-concept Android application and successfully demonstrated this attack on real-world payment terminals. Second, criminals can trick the terminal into accepting an unauthentic offline transaction, which the issuing bank should later decline, after the criminal has walked away with the goods. This attack is possible for implementations following the standard, although we did not test it on actual terminals for ethical reasons. Finally, we propose and verify improvements to the standard that prevent these attacks, as well as any other attacks that violate the considered security properties. The proposed improvements can be easily implemented in the terminals and do not affect the cards in circulation.

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arXiv2020

Differential privacy with partial knowledge

Differential privacy offers formal quantitative guarantees for algorithms over datasets, but it assumes attackers that know and can influence all but one record in the database. This assumption often vastly overapproximates the attackers' actual strength, resulting in unnecessarily poor utility. Recent work has made significant steps towards privacy in the presence of partial background knowledge, which can model a realistic attacker's uncertainty. Prior work, however, has definitional problems for correlated data and does not precisely characterize the underlying attacker model. We propose a practical criterion to prevent problems due to correlations, and we show how to characterize attackers with limited influence or only partial background knowledge over the dataset. We use these foundations to analyze practical scenarios: we significantly improve known results about the privacy of counting queries under partial knowledge, and we show that thresholding can provide formal guarantees against such weak attackers, even with little entropy in the data. These results allow us to draw novel links between k-anonymity and differential privacy under partial knowledge. Finally, we prove composition results on differential privacy with partial knowledge, which quantifies the privacy leakage of complex mechanisms. Our work provides a basis for formally quantifying the privacy of many widely-used mechanisms, e.g. publishing the result of surveys, elections or referendums, and releasing usage statistics of online services.

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arXiv2020

Igloo: Soundly Linking Compositional Refinement and Separation Logic for Distributed System Verification

Lighthouse projects such as CompCert, seL4, IronFleet, and DeepSpec have demonstrated that full verification of entire systems is feasible by establishing a refinement relation between an abstract system specification and an executable implementation. Existing approaches however impose severe restrictions on either the abstract system specifications due to their limited expressiveness or versatility, or on the executable code due to their reliance on suboptimal code extraction or inexpressive program logics. We propose a novel methodology that combines the compositional refinement of abstract, event-based models of distributed systems with the verification of full-fledged program code using expressive separation logics, which support features of realistic programming languages like mutable heap data structures and concurrency. The main technical contribution of our work is a formal framework that soundly relates event-based system models to program specifications in separation logics, such that successful verification establishes a refinement relation between the model and the code. We formalized our framework, Igloo, in Isabelle/HOL. Our framework enables the sound combination of tools for protocol development with existing program verifiers. We report on three case studies, a leader election protocol, a replication protocol, and a security protocol, for which we refine formal requirements into program specifications (in Isabelle/HOL) that we implement in Java and Python and prove correct using the VeriFast and Nagini tools.

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arXiv2020

A Theory of Black-Box Tests

The purpose of testing a system with respect to a requirement is to refute the hypothesis that the system satisfies the requirement. We build a theory of tests and refutation based on the elementary notions of satisfaction and refinement. We use this theory to characterize the requirements that can be refuted through black-box testing and, dually, verified through such tests. We consider refutation in finite time and obtain the finite falsifiability of hyper-safety temporal requirements as a special case. We extend our theory with computational constraints and separate refutation from enforcement in the context of temporal hyper-properties. Overall, our theory provides a basis to analyze the scope and reach of black-box tests and to bridge results from diverse areas including testing, verification, and enforcement.

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arXiv2020

Dispute Resolution in Voting

In voting, disputes arise when a voter claims that the voting authority is dishonest and did not correctly process his ballot while the authority claims to have followed the protocol. A dispute can be resolved if any third party can unambiguously determine who is right. We systematically characterize all relevant disputes for a generic, practically relevant, class of voting protocols. Based on our characterization, we propose a new definition of dispute resolution for voting that accounts for the possibility that both voters and the voting authority can make false claims and that voters may abstain from voting. A central aspect of our work is timeliness: a voter should possess the evidence required to resolve disputes no later than the election's end. We characterize what assumptions are necessary and sufficient for timeliness in terms of a communication topology for our voting protocol class. We formalize the dispute resolution properties and communication topologies symbolically. This provides the basis for verification of dispute resolution for a broad class of protocols. To demonstrate the utility of our model, we analyze a mixnet-based voting protocol and prove that it satisfies dispute resolution as well as verifiability and receipt-freeness. To prove our claims, we combine machine-checked proofs with traditional pen-and-paper proofs.

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arXiv2020

Decentralized Privacy-Preserving Proximity Tracing

This document describes and analyzes a system for secure and privacy-preserving proximity tracing at large scale. This system, referred to as DP3T, provides a technological foundation to help slow the spread of SARS-CoV-2 by simplifying and accelerating the process of notifying people who might have been exposed to the virus so that they can take appropriate measures to break its transmission chain. The system aims to minimise privacy and security risks for individuals and communities and guarantee the highest level of data protection. The goal of our proximity tracing system is to determine who has been in close physical proximity to a COVID-19 positive person and thus exposed to the virus, without revealing the contact's identity or where the contact occurred. To achieve this goal, users run a smartphone app that continually broadcasts an ephemeral, pseudo-random ID representing the user's phone and also records the pseudo-random IDs observed from smartphones in close proximity. When a patient is diagnosed with COVID-19, she can upload pseudo-random IDs previously broadcast from her phone to a central server. Prior to the upload, all data remains exclusively on the user's phone. Other users' apps can use data from the server to locally estimate whether the device's owner was exposed to the virus through close-range physical proximity to a COVID-19 positive person who has uploaded their data. In case the app detects a high risk, it will inform the user.

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arXiv2020

SoK: Delegation and Revocation, the Missing Links in the Web's Chain of Trust

The ability to quickly revoke a compromised key is critical to the security of any public-key infrastructure. Regrettably, most traditional certificate revocation schemes suffer from latency, availability, or privacy problems. These problems are exacerbated by the lack of a native delegation mechanism in TLS, which increasingly leads domain owners to engage in dangerous practices such as sharing their private keys with third parties. We analyze solutions that address the long-standing delegation and revocation shortcomings of the web PKI, with a focus on approaches that directly affect the chain of trust (i.e., the X.509 certification path). For this purpose, we propose a 19-criteria framework for characterizing revocation and delegation schemes. We also show that combining short-lived delegated credentials or proxy certificates with an appropriate revocation system would solve several pressing problems.

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arXiv2020

A Formal Analysis of 5G Authentication

Mobile communication networks connect much of the world's population. The security of users' calls, SMSs, and mobile data depends on the guarantees provided by the Authenticated Key Exchange protocols used. For the next-generation network (5G), the 3GPP group has standardized the 5G AKA protocol for this purpose. We provide the first comprehensive formal model of a protocol from the AKA family: 5G AKA. We also extract precise requirements from the 3GPP standards defining 5G and we identify missing security goals. Using the security protocol verification tool Tamarin, we conduct a full, systematic, security evaluation of the model with respect to the 5G security goals. Our automated analysis identifies the minimal security assumptions required for each security goal and we find that some critical security goals are not met, except under additional assumptions missing from the standard. Finally, we make explicit recommendations with provably secure fixes for the attacks and weaknesses we found.

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arXiv2019

Runtime Verification over Out-of-order Streams

We present an approach for verifying systems at runtime. Our approach targets distributed systems whose components communicate with monitors over unreliable channels, where messages can be delayed, reordered, or even lost. Furthermore, our approach handles an expressive specification language that extends the real-time logic MTL with freeze quantifiers for reasoning about data values. The logic's main novelty is a new three-valued semantics that is well suited for runtime verification as it accounts for partial knowledge about a system's behavior. Based on this semantics, we present online algorithms that reason soundly and completely about streams where events can occur out of order. We also evaluate our algorithms experimentally. Depending on the specification, our prototype implementation scales to out-of-order streams with hundreds to thousands of events per second.

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arXiv2019

The Next 700 Policy Miners: A Universal Method for Building Policy Miners

A myriad of access control policy languages have been and continue to be proposed. The design of policy miners for each such language is a challenging task that has required specialized machine learning and combinatorial algorithms. We present an alternative method, universal access control policy mining (Unicorn). We show how this method streamlines the design of policy miners for a wide variety of policy languages including ABAC, RBAC, RBAC with user-attribute constraints, RBAC with spatio-temporal constraints, and an expressive fragment of XACML. For the latter two, there were no known policy miners until now. To design a policy miner using Unicorn, one needs a policy language and a metric quantifying how well a policy fits an assignment of permissions to users. From these, one builds the policy miner as a search algorithm that computes a policy that best fits the given permission assignment. We experimentally evaluate the policy miners built with Unicorn on logs from Amazon and access control matrices from other companies. Despite the genericity of our method, our policy miners are competitive with and sometimes even better than specialized state-of-the-art policy miners. The true positive rates of policies we mined differ by only 5% from the policies mined by the state of the art and the false positive rates are always below 5%. In the case of ABAC, it even outperforms the state of the art.

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arXiv2018

Cardinality Estimators do not Preserve Privacy

Cardinality estimators like HyperLogLog are sketching algorithms that estimate the number of distinct elements in a large multiset. Their use in privacy-sensitive contexts raises the question of whether they leak private information. In particular, can they provide any privacy guarantees while preserving their strong aggregation properties? We formulate an abstract notion of cardinality estimators, that captures this aggregation requirement: one can merge sketches without losing precision. We propose an attacker model and a corresponding privacy definition, strictly weaker than differential privacy: we assume that the attacker has no prior knowledge of the data. We then show that if a cardinality estimator satisfies this definition, then it cannot have a reasonable level of accuracy. We prove similar results for weaker versions of our definition, and analyze the privacy of existing algorithms, showing that their average privacy loss is significant, even for multisets with large cardinalities. We conclude that strong aggregation requirements are incompatible with any reasonable definition of privacy, and that cardinality estimators should be considered as sensitive as raw data. We also propose risk mitigation strategies for their real-world applications.

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arXiv2018

Exploring Website Location as a Security Indicator

Authenticating websites is an ongoing problem for users. Recent proposals have suggested strengthening current server authentication methods by incorporating website location as a comprehensible additional trust factor. In this work, we explore users' acceptance of location information and how it affects decision-making for security and privacy. We conducted a series of qualitative interviews to learn how location can be integrated into users' decision-making for security, and we designed a security indicator to alert the user to changes in website locations. We evaluated our tool in a 44-participant user study and found that users were less likely to perform security-sensitive tasks when alerted to location changes. Our results suggest that website location can be used as an effective indicator for users' security assessments.

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arXiv2017

Runtime Verification of Temporal Properties over Out-of-order Data Streams

We present a monitoring approach for verifying systems at runtime. Our approach targets systems whose components communicate with the monitors over unreliable channels, where messages can be delayed or lost. In contrast to prior works, whose property specification languages are limited to propositional temporal logics, our approach handles an extension of the real-time logic MTL with freeze quantifiers for reasoning about data values. We present its underlying theory based on a new three-valued semantics that is well suited to soundly and completely reason online about event streams in the presence of message delay or loss. We also evaluate our approach experimentally. Our prototype implementation processes hundreds of events per second in settings where messages are received out of order.

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arXiv2017

Securing Databases from Probabilistic Inference

Databases can leak confidential information when users combine query results with probabilistic data dependencies and prior knowledge. Current research offers mechanisms that either handle a limited class of dependencies or lack tractable enforcement algorithms. We propose a foundation for Database Inference Control based on ProbLog, a probabilistic logic programming language. We leverage this foundation to develop Angerona, a provably secure enforcement mechanism that prevents information leakage in the presence of probabilistic dependencies. We then provide a tractable inference algorithm for a practically relevant fragment of ProbLog. We empirically evaluate Angerona's performance showing that it scales to relevant security-critical problems.

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arXiv2016

Access Control Synthesis for Physical Spaces

Access-control requirements for physical spaces, like office buildings and airports, are best formulated from a global viewpoint in terms of system-wide requirements. For example, "there is an authorized path to exit the building from every room." In contrast, individual access-control components, such as doors and turnstiles, can only enforce local policies, specifying when the component may open. In practice, the gap between the system-wide, global requirements and the many local policies is bridged manually, which is tedious, error-prone, and scales poorly. We propose a framework to automatically synthesize local access control policies from a set of global requirements for physical spaces. Our framework consists of an expressive language to specify both global requirements and physical spaces, and an algorithm for synthesizing local, attribute-based policies from the global specification. We empirically demonstrate the framework's effectiveness on three substantial case studies. The studies demonstrate that access control synthesis is practical even for complex physical spaces, such as airports, with many interrelated security requirements.

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arXiv2016

Strong and Provably Secure Database Access Control

Existing SQL access control mechanisms are extremely limited. Attackers can leak information and escalate their privileges using advanced database features such as views, triggers, and integrity constraints. This is not merely a problem of vendors lagging behind the state-of-the-art. The theoretical foundations for database security lack adequate security definitions and a realistic attacker model, both of which are needed to evaluate the security of modern databases. We address these issues and present a provably secure access control mechanism that prevents attacks that defeat popular SQL database systems.

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arXiv2013

Role Mining with Probabilistic Models

Role mining tackles the problem of finding a role-based access control (RBAC) configuration, given an access-control matrix assigning users to access permissions as input. Most role mining approaches work by constructing a large set of candidate roles and use a greedy selection strategy to iteratively pick a small subset such that the differences between the resulting RBAC configuration and the access control matrix are minimized. In this paper, we advocate an alternative approach that recasts role mining as an inference problem rather than a lossy compression problem. Instead of using combinatorial algorithms to minimize the number of roles needed to represent the access-control matrix, we derive probabilistic models to learn the RBAC configuration that most likely underlies the given matrix. Our models are generative in that they reflect the way that permissions are assigned to users in a given RBAC configuration. We additionally model how user-permission assignments that conflict with an RBAC configuration emerge and we investigate the influence of constraints on role hierarchies and on the number of assignments. In experiments with access-control matrices from real-world enterprises, we compare our proposed models with other role mining methods. Our results show that our probabilistic models infer roles that generalize well to new system users for a wide variety of data, while other models' generalization abilities depend on the dataset given.

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arXiv2012

Abstract Data Types in Event-B - An Application of Generic Instantiation

Integrating formal methods into industrial practice is a challenging task. Often, different kinds of expertise are required within the same development. On the one hand, there are domain engineers who have specific knowledge of the system under development. On the other hand, there are formal methods experts who have experience in rigorously specifying and reasoning about formal systems. Coordination between these groups is important for taking advantage of their expertise. In this paper, we describe our approach of using generic instantiation to facilitate this coordination. In particular, generic instantiation enables a separation of concerns between the different parties involved in developing formal systems.

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