Instructors: Prof. Ph.D. Sebastian Faust; M.Sc. Elena Micheli; M.Sc. Kathrin Wirschem

Event type: Integrated Course

Org-unit: Dept. 20 - Computer Science

Displayed in timetable as: Einf Krypto

Subject:

Crediting for:

Hours per week: 4

Language of instruction: German

Min. | Max. participants: - | -

Course Contents:

• Perfect Security
• Different definitions
• One Time Pad and its security
• Limitations
• Private Key Encryption
• Computationally secure Priv. Key Encryption
• Pseudo Random Generators (PRG)
• Building secure Encryption scheme from PRG
• Practical PRGs - Stream Ciphers
• Stronger security notions
• Pseudo Random Functions (PRF)
• CPA security
• Pseudorandom Permutation (PRP) and Block Cipher
• Practical construction of Block Ciphers
• Message Authentication Codes
• Hash Functions
• Cryptographic Assumptions
• Key Agreement
• Introduction to Public Key Encryption
• Public Key Encryption Schemes - RSA
• Signatures

Literature:

• Your notes, exercise sheets, slides
• Jonathan Katz, Yehuda Lindell: Introduction to Modern Cryptography
• A graduate course on applied cryptography

Preconditions:

• Probability Theory Basics

Further Grading Information: WS17/18: Einführung in die Kryptographie

Database management systems (DBMS) in the cloud are the backbone for managing large volumes of data efficiently and thus play a central role in business and science today. For providing high performance, many of the most complex DBMS components such as query optimizers or schedulers involve solving non-trivial problems.

To tackle such problems, very recent work has outlined a new direction of so-called learned DBMS components where AI-based methods are used to replace and enhance core DBMS components, which has been shown to provide significant performance benefits. This route is particularly interesting since Cloud vendors such as Google, Amazon, and Microsoft are already applying these techniques to optimize the performance of their cloud data systems.

Besides learned DBMS components, AI has been used to improve many other data management-related tasks. For example, classical data engineering tasks like error detection, missing value imputation, and data augmentation typically cause high manual overheads and can be automated with AI. Finally, AI has also been used to extend databases through better data access interfaces (e.g., natural language querying and chatbots for data) or by supporting data beyond structured tabular data (i.e., text and images).

This seminar is designed to introduce students to the foundational concepts of using AI for data management. The course will include a mini lecture series that provides the necessary background on AI in data management, preparing students for the seminar tasks. The seminar is divided into two parts, each focusing on key themes as introduced above: learned DBMS components and the application of AI for data engineering. Students will engage in practical tasks related to these topics, as outlined below.

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Dieser Moodle-Kurs dient dem Austausch von Materialien, Folien, Aufzeichnungen, Übungen, Quizzen etc. 

Kurs: Informationssicherheitsmanagement
Kürzel: ISM
TUCaN-ID: 20-00-1123
Dozent: Maximilian Müller
Inhalte: Management der organisatorischen und prozessualen Informationssicherheit in Unternehmen, Fokus dabei weniger auf formale oder technische IT-Sicherheit sondern mehr auf Themen der organisatorischen Informationssicherheit, mit denen man auch als ISO/ISB (Information Security Officer/Informationssicherheitsbeauftragter) in einem Unternehmen zu tun hat.
Wann: Montags, 09:50 - 11:20 Uhr
Wo: S1|01 A4
Klausur: Dienstag, der 18. Februar von 12:00 - 13:30 (90 min)

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Modern cryptographic algorithms provide a reasonable level of security against known mathematical and cryptanalytic attacks. These cryptographic primitives are implemented on different platforms to be used in a security-enabled applications. Such a realization is done by implementing the desired cryptographic algorithm using some program code (in software) or using logic elements/circuits (in hardware). Physical access of the users to the cryptographic devices (e.g., a smartcard used for payment, a contactless card used for authentication, or a smartphone) where a secret key is embedded, led to a new form of attacks called physical attacks. This kind of attacks aims at extracting the secret key used by the cryptographic algorithm from the target implementation. Breaking a system by means of a physical attack does not infer to the weakness of the algorithm, but of the implementation. Therefore, considering such kinds of attacks as a potential risk for the security is a must when designing a cryptographic device and weaknesses in that regard need to be avoided from the start. The goal of this lecture is to give an overview about the known physical attacks and most considerably the schemes developed to counter such kinds of attacks. In the first part of the lecture different kinds of physical attacks are introduced, while in the second part we focus on countermeasures and the methods to make implementations resistant against known physical attacks.

Recommended: basic knowledge of digital circuit design, basic knowledge of data security and cryptography, solid programming ability in at least one programming language (e.g. C++), basic knowledge of computer architecture, basic knowledge of signal processing.

Scalable Data Management Systems Winter Semester 2024/25

Contents:

• Database Architectures
• Parallel and Distributed Databases
• Cloud Databases
• Data Warehousing
• MapReduce and Hadoop
• Spark and its Ecosystem
• Optional: NoSQL Databases, Stream Processing, Graph Databases, Scalable Machine Learning

Chair of Implementation Security offers a seminar for Master students. As a side note, previous knowledge in the field of IT Security or cryptography is recommended, but is not a must. The topics offered by the group are assigned via moodle.