FUNDED PROJECTS
Part of CISPA's research is covered by third-party funding. Our researchers recruit these funds in competitive procedures - alone or in cooperation with other applicants.
IMPACT
ERC Synergy Grant imPACT – Privacy, Accountability, Compliance, and Trust in Tomorrow’s Internet
The Internet has evolved from a mere communication network used by tens of millions of people two decades ago, to a global multimedia platform for communication, social networking, entertainment, education, trade and political activism with more than two billion users. This transformation has brought tremendous benefits to society, but has also created entirely new threats to privacy, safety, law enforcement, freedom of information and freedom of speech. In today’s Internet, principals are amorphous, identities can be fluid, users participate and exchange information as peers, and data is processed on global third-party platforms. Existing models and techniques for security and privacy, which assume trusted infrastructure and well-defined policies, principals and roles, fail to fully address this challenge.
The imPACT project addresses the challenge of providing privacy, accountability, compliance and trust (PACT) in tomorrow’s Internet, using a cross-disciplinary and synergistic approach to understanding and mastering the different roles, interactions and relationships of users and their joint effect on the four PACT properties. The focus is on principles and methodologies that are relevant to the needs of individual Internet users, have a strong potential to lead to practical solutions and address the funda-mental long-term needs of the future Internet. We take on this challenge with a team of researchers from relevant subdisciplines within computer science, and with input from outside experts in law, so-cial sciences, economics and business. The team of PIs consists of international leaders in privacy and security, experimental distributed systems, formal methods, program analysis and verification, and database systems. By teaming up and committing ourselves to this joint research, we are in a unique position to meet the grand challenge of unifying the PACT properties and laying a new foundation for their holistic treatment.
Funding Body
Partner
Management
Duration
01.02.2015-31.01.2021
Funding Code
Grant agreement ID: 610150
Research Area
Funding Body
Partner
Incubator
CISPA founding incubator for start-ups in the IT security sector
CISPA's research topics contain an enormous potential for technology transfer into industrial application. CISPA has already been in active exchange with partners from industry and business for several years. Under the assumption that utilization in newly founded companies is the most direct form of knowledge and technology transfer, the expansion of the start-up incubator provides the opportunity to expand specialized structures for the explicit support of spin-offs.
The aim of the project is therefore to expand these initiatives conceptually and anchor them structurally in order to create a highly creative environment in the immediate vicinity of CISPA and in the vicinity of the Saarland Informatics Campus.
The BMBF's funding of measures primarily provides for the following areas: Raising awareness, project initiation, project funding, scaling and the overall management of the incubator.
Pro-Gene-Gen
Protecting Genetic Data with Synthetic Cohorts from Deep Generative Models
Genetic data is highly privacy sensitive information and therefore is protected under stringent legal regulations, making them burdensome to share. However, leveraging genetic information bears great potential in diagnosis and treatment of diseases and is essential for personalized medicine to become a reality. While privacy preserving mechanisms have been introduced, they either pose significant overheads or fail to fully protect the privacy of sensitive patient data. This reduces the ability to share data with the research community which hinders scientific discovery as well as reproducibility of results. Hence, we propose a different approach using synthetic data sets that share the properties of patient data sets while respecting the privacy. We achieve this by leveraging the latest advances in generative modeling to synthesize virtual cohorts. Such synthetic data can be analyzed with established tool chains, repeated access does not affect the privacy budget and can even be shared openly with the research community. While generative modeling of high dimensional data like genetic data has been prohibitive, latest developments in deep generative models have shown a series of success stories on a wide range of domains. The project will provide tools for generative modeling of genetic data as well as insights into the long-term perspective of this technology to address open domain problems. The approaches will be validated against existing analysis that are not privacy preserving. We will closely collaborate with the scientific community and propose guidelines how to deploy and experiment with approaches that are practical in the overall process of scientific discovery. This unique project will be the first to allow the generation of synthetic high-dimensional genomic information to boost privacy compliant data sharing in the medical community.
Management
Duration
1.8.2020-31.07.2023
Members
Funding Code
ZT-1-PF-5-23
Website
TFDA
Trustworthy Federated Data Analytics
To solve future grand challenges, data, computational power and analytics expertise need to be brought together at unprecedented scale. The need for data has become even larger in the context of recent advances in machine learning. Therefore, data-centric digital systems commonly exhibit a strong tendency towards centralized structures. While data centralization can greatly facilitate analy-sis, it also comes with several intrinsic disadvantages and threats not only from a technical but more importantly also from a legal, political and ethical perspective. Rooting in sophisticated security or trust requirements, overcoming these issues is cumbersome and time consuming. As a consequence, many research projects are substantially hindered, fail or are simply not addressed. In this interdisci-plinary project we aim at facilitating the implementation of decentralized, cooperative data analytics architectures within and beyond Helmholtz by addressing the most relevant issues in such scenarios.
Trustworthy Federated Data Analytics (TFDA) will facilitate bringing the algorithms to the data in a trustworthy and regulatory compliant way instead of going a data-centric way. TFDA will address the technical, methodical and legal aspects when ensuring trustworthiness of analysis and transparency regarding the analysis in- and outputs without violating privacy constraints. To demonstrate applica-bility and to ensure the adaptability of the methodological concepts, we will validate our develop-ments in the use case “Federated radiation therapy study” (Health) before disseminating the results.
Management
Duration
01.12.2019–30.11.2022
Members
Funding Code
ZT-I-0014
Research Area
AlmaCrypt
Algorithmic and Mathematical Cryptology
Cryptology is a foundation of information security in the digital world. Today's internet is protected by a form of cryptography based on complexity theoretic hardness assumptions. Ideally, they should be strong to ensure security and versatile to offer a wide range of functionalities and allow efficient implementations. However, these assumptions are largely untested and internet security could be built on sand. The main ambition of Almacrypt is to remedy this issue by challenging the assumptions through an advanced algorithmic analysis.
In particular, this proposal questions the two pillars of public-key encryption: factoring and discrete logarithms. Recently, the PI contributed to show that in some cases, the discrete logarithm problem is considerably weaker than previously assumed. A main objective is to ponder the security of other cases of the discrete logarithm problem, including elliptic curves, and of factoring. We will study the generalization of the recent techniques and search for new algorithmic options with comparable or better efficiency. We will also study hardness assumptions based on codes and subset-sum, two candidates for post-quantum cryptography. We will consider the applicability of recent algorithmic and mathematical techniques to the resolution of the corresponding putative hard problems, refine the analysis of the algorithms and design new algorithm tools. Cryptology is not limited to the above assumptions: other hard problems have been proposed to aim at post-quantum security and/or to offer extra functionalities. Should the security of these other assumptions become critical, they would be added to Almacrypt's scope. They could also serve to demonstrate other applications of our algorithmic progress. In addition to its scientific goal, Almacrypt also aims at seeding a strengthened research community dedicated to algorithmic and mathematical cryptology.
Management
Duration
01/01/2016-31/21/2021
Funding Code
ERC Advanced Grants 669891
Research Area
Systematicgraph
Systematic mapping of the complexity landscape of hard algorith-mic graph problems
The goal of the SYSTEMATICGRAPH project is to put the search for tractable algorithmic graph prob-lems into a systematic and methodological framework: instead of focusing on specific sporadic prob-lems, we intend to obtain a unified algorithmic understanding by mapping the entire complexity landscape of a particular problem domain. A dichotomy theorem is a complete classification result that characterizes the complexity of each member of a family of problems: it identifies all the cases that admit efficient algorithms and proves that all the other cases are computationally hard. The pro-ject will demonstrate that such a complete classification is feasible for a wide range of graph prob-lems coming from areas such as finding patterns, routing, and survivable network design, and novel algorithmic results and new levels of algorithmic understanding can be achieved even for classic and well-studied problems.
Management
Duration
01.07.2017-30.06.2022
Funding Code
Grant agreement ID: 725978
Research Area
DARS
Detecting Anomalies in Reactive Systems
What exactly makes a program malicious? In this project, the hypothesis that a “malicious” program is one that does not work as advertised is investigated. The idea is to make use of large collections of programs, especially apps in app stores, and to learn associations between advertised and implemented behavior from them. As advertised behavior, use of natural language descriptions as presented in user interface elements is made; as implemented behavior, the functionality triggered by these UI elements is checked.
The result is a model of actions and reactions that characterizes “normal” behavior. Given a new app, its model is automatically checked on whether the observed actions and reactions are “normal” or not. The problems related to security can thus be highlighted (the implementation does not work as adver-tised) as well as usability (the description does not match the implementation). During execution, a sandbox detects “abnormal” and explicitly disallowed sequences, and blocks the associated resource accesses and UI elements: “The ‘Download’ button is greyed out because it sends your address book to a server in Bezerkistan”.
The project brings together expertise in program analysis, test generation, natural language processing, model inference, and model checking. It makes significant contributions in all these fields to achieve its overall goal of detecting and preventing abnormal behavior in reactive systems.
Management
Duration
01.01.2019 – 31.10.2021
Members
Funding Code
DFG ZE 509/9-1
Research Area
Fuzzing
Efficient Detection of Software Vulnerabilities using Fuzzing
Fuzzing – testing software through randomly generated inputs – is one of the premier methods to discover software vulnerabilities. Fuzzing is easy to deploy; once set up, it can be left running for days and weeks, continuously testing the system with one input after another. Fuzzing also has no false positives: any input that crashes the program triggers a real vulnerability that can be exploited by attackers, if only for a denial of service attack.
Fuzzing is slow, though. The overwhelming majority of randomly generated inputs is invalid and will thus be rejected by the program under test. This can still detect errors, notably in the routines for parsing and rejecting inputs. In order to reach deeper functionality after input processing, though, it is necessary to have inputs that are syntactically valid.
The traditional means to produce valid inputs is to formally specify the input language using formal languages such as regular expressions and grammars – well-established and well-understood formal-isms with a sound and detailed theoretical foundation and plenty of applications in practice. Specifying an input language, however, is a huge manual effort, ranging from days for simple data formats to months for complex input languages.
In the past years, the group of PI Zeller has developed a number of techniques that can automatically extract grammars from a given program and a set of sample inputs and shown how to construct extremely efficient fuzzers from these grammars. These techniques are so mature they are even avail-able as open source in a recently published textbook. Yet, the grammar learners still depend on a comprehensive set of samples that cover every feature of the input space.
Therefore the aim of the project is to create test generators that specifically target input processors – that is, lexers (tools that compose input characters into words) as well as parsers (tools that compose sequences of words into syntactical structures, such as sentences in natural language). His approach is to feed some trivial invalid input (say, the string “x”) into a program and then to dynamically track the comparisons this program undertakes before rejecting the input as invalid.
Management
Duration
01.06.2019 – 30.11.2021
Members
Research Area
Security Flaws
Learning to Discover Security Flaws in Stateful Programs
Our 13-trillion dollar digital economy runs on open-source software written by ambitious developers often in their spare time. Many software systems expect their inputs to arrive in a specific order and in a well-structured manner. The order is specified by a protocol while the structure is specified by a grammar. For instance, most websites are available via the SSL protocol (https) which requires a sequence of 13 distinct messages only to establish a secure connection.
Each message starts with a 5-byte header specifying message type, version, and length. This makes it very difficult to automatically test such critical systems, to identify security flaws at scale.
The objective of this project is to develop automated techniques that infer input protocols and gram-mars automatically. The inferred artifacts will then be used to test the software system more effectively, in turn. The project will use lightweight program analysis and instrumentation for inference at runtime. It is further envisioned a unification of protocol and grammar into a single representation of both, structure and sequence.
The exchange seeks to strengthen the existing collaboration, and to synthesize recent independent works across both teams on greybox fuzzing and grammar mining.
Management
Duration
01.01.2020 – 31.12.2021
Members
Funding Code
57511650
Research Area
