Overall Objectives
Research Program
Application Domains
Highlights of the Year
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results

Other research results

Privacy and Security: Information-Theoretical Quantification of Security Properties

Participants : Axel Legay, Fabrizio Biondi, Olivier Zendra, Thomas Given-Wilson, Annelie Heuser, Sean Sedwards, Jean Quilbeuf, Mike Enescu.

Information theory provides a powerful quantitative approach to measuring security and privacy properties of systems. By measuring the information leakage of a system security properties can be quantified, validated, or falsified. When security concerns are non-binary, information theoretic measures can quantify exactly how much information is leaked. The knowledge of such information is strategic in the developments of component-based systems.

The quantitative information-theoretical approach to security models the correlation between the secret information of the system and the output that the system produces. Such output can be observed by the attacker, and the attacker tries to infer the value of the secret information by combining this information with their prior knowledge of the system.

Armed with the produced output of the system, the attacker tries to infer information about the secret information that produced the output. The quantitative analysis we consider defines and computes how much information the attacker can expect to infer (typically measured in bits). This expected leakage of bits is the information leakage of the system. This is computed by symbolically exploring the code to be analyzed, and using the symbolic constraints accumulated over the output together with a model counting algorithm to quantify the leakage.

The quantitative approach generalizes the qualitative approach and thus provides superior analysis. In particular, a system respects non-interference if and only if its leakage is equal to zero. In practice very few systems respect non-interference, and for those that don't it is imperative to be able to distinguish between the systems leaking very small amounts of secret information and systems leaking a significant amount of secret information, since only the latter are considered to pose a security vulnerability to the system.

While quantitative leakage computation is a powerful technique to detect security vulnerabilities, computing the leakage of complex programs written in low-level languages is a hard and computationally intensive task. The most common language for low-level implementation of security protocols is C, due to its efficiency, hence much of the effort in developing tools to detect vulnerabilities in source code focus on C. Recently, we have improved the state of the art in leakage quantification from C programs by proposing the usage of approximated model counting instead of precise model counting. We have shown how the approximation can improve the efficiency of leakage quantification by orders of magnitude against a logarithmic decrease in the precision of the result, often producing the same result as precise model counters much faster, and often being able to analyze cases where precise model counters would have failed. We demonstrated this technique by providing the first quantitative leakage analysis of the C code of the Heartbleed bug, showing that our technique can detect the bug in the code.

A different but equally interesting approach is followed by our new HyLEak tool. HyLeak is also able to analyze a system and compute its information leakage, i.e. the amount of information that an observer would gain by about the value of system's secret by observing its output. Contrarily to other techniques, HyLeak can analyze randomized systems, and correctly distinguish between the randomness injected in the system and the uncertainty on the secret value. This allows HyLeak to be used both on systems with explicit randomization and systems that depend on stochastic properties, like cyber-physical systems.

HyLeak uses static code analysis to divide the system to be analyzed in components. For each component, HyLeak evaluates whether it is more convenient to analyze the component using precise or statistical analysis. Each component is analyzed with the most appropriate strategy, and then the results for all components are combined together and information leakage is estimated.

The hybrid approach provides better results than both the precise and the statistical ones in terms of computation time and precision of the result. Also, it bridges the gap between cheap but imprecise statistical techniques and precise but expensive formal techniques, allowing the user to control the required precision of the result according to the computation time they have available. We evaluated HyLeak against QUAIL's precise approach and the statiatical approach implemented in the LeakWatch tool, showing that HyLeak outperforms them both. HyLeak is open source and available at

Applied to shared-key cryptosystems, the information-theoretical approach allows precise reasoning about the information leakage of any secret information in the system including, the key, and the message. Recent work on max-equivocation has generalised perfect secrecy and shown the maximum achievable theoretic bounds for the security of the key and message. Achieving these theoretic maximal bounds has been proven to be achievable by Apollonian Cell Encoders (ACEs). ACEs not only allow the maximum security possible in a shared-key cryptosystem, but also allow for infinite key reuse when the key has less entropy than the message. Further, ACEs are straightforward to construct and have a compact representation making them feasible to use in practice.

Another application is to use information leakage to reason about leakage through shared resources, representing various side-channel attacks. Developmens here allow for the formalising of the leakage model through shared resources, and quantifying how significant the leakage can be. This improves on the state-of-the-art that uses only qualified leakage, and so can be precise about how much is leakage through a shared resource. Such quantification of leakage allows for scheduling of the shared resource to exploit this information to minimise leakage. Such minimisation of leakage allows for scheduling and utilisation of resources that would fail a simple quanlified test, providing solutions when prior state-of-the-art would claim impossibility. Further, a reasoned trade-off can be made between acceptable leakage and utility of the shared resource, allowing solutions that are acceptable even if not perfect.

[53] (C; submitted)

Preserving privacy of private communication against an attacker is a fundamental concern of computer science security. Unconditional encryption considers the case where an attacker has unlimited computational power, hence no complexity result can be relied upon for encryption. Optimality criteria are defined for the best possible encryption over a general collection of entropy measures. This paper introduces Apollonian cell encoders, a class of shared-key cryptosystems that are proven to be universally optimal. In addition to the highest possible security for the message, Apollonian cell encoders prove to have perfect secrecy on their key allowing unlimited key reuse. Conditions for the existence of Apollonian cell encoders are presented, as well as a constructive proof. Further, a compact representation of Apollonian cell encoders is presented, allowing for practical implementation.

[18] (C)

High-security processes have to load confidential information into shared resources as part of their operation. This confidential information may be leaked (directly or indirectly) to low-security processes via the shared resource. This paper considers leakage from high-security to low-security processes from the perspective of scheduling. The workflow model is here extended to support preemption, security levels, and leakage. Formalization of leakage properties is then built upon this extended model, allowing formal reasoning about the security of schedulers. Several heuristics are presented in the form of compositional preprocessors and postprocessors as part of a more general scheduling approach. The effectiveness of such heuristics are evaluated experimentally, showing them to achieve significantly better schedulability than the state of the art. Modeling of leakage from cache attacks is presented as a case study.

[52] (C)

Quantitative information flow measurement techniques have been proven to be successful in detecting leakage of confidential information from programs. Modern approaches are based on formal methods, relying on program analysis to produce a SAT formula representing the program's behavior, and model counting to measure the possible information flow. However, while program analysis scales to large codebases like the OpenSSL project, the formulas produced are too complex for analysis with precise model counting. In this paper we use the approximate model counter ApproxMC2 to quantify information flow. We show that ApproxMC2 is able to provide a large performance increase for a very small loss of precision, allowing the analysis of SAT formulas produced from complex code. We call the resulting technique ApproxFlow and test it on a large set of benchmarks against the state of the art. Finally, we show that ApproxFlow can evaluate the leakage incurred by the Heartbleed OpenSSL bug, contrarily to the state of the art.

[20] (C)

We present HyLeak, a tool for reasoning about the quantity of information leakage in programs. The tool takes as input the source code of a program and analyzes it to estimate the amount of leaked information measured by mutual information. The leakage estimation is mainly based on a hybrid method that combines precise program analysis with statistical analysis using stochastic program simulation. This way, the tool combines the best of both symbolic and randomized techniques to provide more accurate estimates with cheaper analysis, in comparison with the previous tools using one of the analysis methods alone. HyLeak is publicly available and is able to evaluate the information leakage of randomized programs, even when the secret domain is large. We demonstrate with examples that HyLeaks has the best performance among the tools that are able to analyze randomized programs with similarly high precision of estimates.

[54] (J; submitted)

Analysis of a probabilistic system often requires to learn the joint probability distribution of its random variables. The computation of the exact distribution is usually an exhaustive precise analysis on all executions of the system. To avoid the high computational cost of such an exhaustive search, statistical analysis has been studied to efficiently obtain approximate estimates by analyzing only a small but representative subset of the system's behavior. In this paper we propose a hybrid statistical estimation method that combines precise and statistical analyses to estimate mutual information, Shannon entropy, and conditional entropy, together with their confidence intervals. We show how to combine the analyses on different components of the system with different accuracy to obtain an estimate for the whole system. The new method performs weighted statistical analysis with different sample sizes over different components and dynamically finds their optimal sample sizes. Moreover it can reduce sample sizes by using prior knowledge about systems and a new abstraction-then-sampling technique based on qualitative analysis. To apply the method to the source code of a system, we show how to decompose the code into components and to determine the analysis method for each component by overviewing the implementation of those techniques in HyLeak tool. We demonstrate with case studies that the new method outperforms the state of the art in quantifying information leakage.

Security for therapeutical environments

Participants : Axel Legay, Olivier Zendra, Thomas Given-Wilson, Sean Sedwards.

This work is done in the context of the ACANTO EU project. We aim at helping develop robotic assistants to aid mobility of mobility-impaired and elderly adults. These robotic assistants provide a variety of support to their users, including: navigational assistance, social networking, social activity planning, therapeutic regime support, and diagnostic support. In Tamis, we focus on navigational assistance and social activities, as together they yield an interesting challenge in human robot interaction. The goal is to help groups of users navigate in a potentially busy dynamic environment, while also maintaining social group cohesion.

A robotic assistant has been developed before in the DALi project, acting selfishly to ensure the safe navigation of a single user. This was achieved by using the social force model and statistical model checking in a reactive planner that frequently replanned and made immediate navigational suggestions to the user. The key operational loop of this solution was to: observe the environment, model the agents in the environment in the social force model, give safety constraints for the user, and then use statistical model checking to find the optimal next move for the user.

Generalising to groups of users poses several significant difficulties. Computationally, the challenge is exponential in the number of users, considering all their possible navigational choices. Incomplete information is normal, since sensors are distributed between robotic assistants and the environment, and communication may fail, leading to different robots having different knowledge of the environment. Maintaining group cohesion is non-trivial, since group composition and position are dynamic and, unlike swarm robotics, no group member can be abandoned. Frequent replanning is necessary since there is minimal control over the users’ actions, which may include ignoring the advise of the robotic assistant

The solution we designed is to abstract away from individual users in favour of groups. This refines the prior solution for a single user. Sensor information is used to obtain traces that provide behavioural information about users and pedestrians in the environment. These traces are clustered into groups that capture both location and motion behaviour. The groups are used as the social particles in the social force model, with parameters adjusted to account for group dynamics. Statistical model checking is used to find the optimal next move for the group containing the user, and the navigation for the optimal next move is displayed to the user. The effectiveness of the group abstraction mechanisms use in this refined algorithm are validated on the BIWI walking pedestrians dataset. This shows they operate correctly and effectively, even improving over human annotations, on real world data of pedestrians in a chaotic environment.

[27] (C)

People with impaired physical and mental ability often find it challenging to negotiate crowded or unfamiliar environments, leading to a vicious cycle of deteriorating mobility and sociability. To address this issue the ACANTO project is developing a robotic assistant that allows its users to engage in therapeutic group social activities, building on work done in the DALi project. Key components of the ACANTO technology are social networking and group motion planning, both of which entail the sharing and broadcasting of information. Given that the system may also make use of medical records, it is clear that the issues of security, privacy, and trust are of supreme importance to ACANTO.

[58] (C; submitted)

The ACANTO project is developing robotic assistants to aid the mobility and recovery of mobility-impaired and older adults. One key feature of the project's robotic assistants is aiding with navigation in chaotic environments. Prior work has solved this for a single user with a single robot, however for therapeutic outcomes ACANTO supports social groups and group activities. Thus these robotic assistants must be able to efficiently support groups of users walking together. This requires an efficient navigation solution that can handle large numbers of users, maintain (de-facto) group cohesion despite unpredictable behaviours, and operate rapidly on embedded devices. We address these challenges by: using sensor information to develop behavioural traces, clustering traces to determine groups, modeling the groups using the social force model, and finding an optimal navigation solution using statistical model checking. The new components of this solution are validated on the ETH Zürich dataset of pedestrians in an open environment.

Mobile air pollution sensor platform for smart-cities

Participant : Laurent Morin.

This work is organized and coordinated by the Chaire “mobilité dans une ville durable” and financed by the Foundation of Rennes 1 (

The purpose of this work is to design and experiment a mobile pollution sensor platform for Smart-Cities in Rennes.

The platform is integrated in the project ROAD (Rennes Open Access to Data ) proposing to development of mobile systems operating the collection and the management of open data in Rennes for a future development of a smart-city. The collaboration is part of an ecosystem developed by the Chair “mobilité dans une ville durable” via the production of multiple experimentations in the city.

In the ROAD project context, the air quality in the city has been identified as one of the major challenge. Air quality improvement can only be achieved with a citizen and political full cooperation and involvement. This experimentation aims at providing an end-to-end urban platform that extends current practices in air quality measurements and allows citizens and policy makers to obtain the data and make informed decisions.

The mobile air pollution sensor platform for smart-cities proposes a innovative IoT architecture introducing the deployment of a small set of advanced and cost-effective sensors around a balanced high-performance/low-power compute unit inside a mobile agent in the city. The compute unit will have to provide the necessary computation power needed to produce advanced analysis and the security management on-site (integrity, authentication, ...).

The mobile sensor platform developments partially started in July 2017, and accelerated in October for a real deployment in buses in 2018. During this period, the core system of the platform was designed, adapted, and partially implemented to offer an operational prototype. This year lead to the design of a suitcase containing a self-sufficient measurement system: a main compute unit, its power supply and power management, and a set of satellite pollution sensors. This achievement was disseminated to the Rennes ecosystem (Rennes Atalante, Rennes Métropole, Inria) through the participation to several meetings and exhibitions.