We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

1. The Advanced Encryption Standard Process.- 2. Preliminaries.- 3. Specification of Rijndael.- 4. Implementation Aspects.- 5. Design Philosophy.- 6. The Data Encryption Standard.- 7. Correlation Matrices.- 8. Difference Propagation.- 9. The Wide Trail Strategy.- 10. Cryptanalysis.- 11. Related Block Ciphers.- Appendices.- A. Propagation Analysis in Galois Fields.- A.1.1 Difference Propagation.- A.l.2 Correlation.- A. 1.4 Functions that are Linear over GF(2).- A.2.1 Difference Propagation.- A.2.2 Correlation.- A.2.4 Functions that are Linear over GF(2).- A.3.3 Dual Bases.- A.4.2 Relationship Between Trace Patterns and Selection Patterns.- A.4.4 Illustration.- A.5 Rijndael-GF.- B. Trail Clustering.- B.1 Transformations with Maximum Branch Number.- B.2 Bounds for Two Rounds.- B.2.1 Difference Propagation.- B.2.2 Correlation.- B.3 Bounds for Four Rounds.- B.4 Two Case Studies.- B.4.1 Differential Trails.- B.4.2 Linear Trails.- C. Substitution Tables.- C.1 SRD.- C.2 Other Tables.- C.2.1 xtime.- C.2.2 Round Constants.- D. Test Vectors.- D.1 KeyExpansion.- D.2 Rijndael(128,128).- D.3 Other Block Lengths and Key Lengths.- E. Reference Code.

The Design of Rijndael: AES - The Advanced Encryption Standard

National Institute of Standards and Technology における超伝導研究及び生活

A morphable model for the synthesis of 3D faces

Modern automobiles are no longer mere mechanical devices; they are pervasively monitored and controlled by dozens of digital computers coordinated via internal vehicular networks. While this transformation has driven major advancements in efficiency and safety, it has also introduced a range of new potential risks. In this paper we experimentally evaluate these issues on a modern automobile and demonstrate the fragility of the underlying system structure. We demonstrate that an attacker who is able to infiltrate virtually any Electronic Control Unit (ECU) can leverage this ability to completely circumvent a broad array of safety-critical systems. Over a range of experiments, both in the lab and in road tests, we demonstrate the ability to adversarially control a wide range of automotive functions and completely ignore driver input\dash including disabling the brakes, selectively braking individual wheels on demand, stopping the engine, and so on. We find that it is possible to bypass rudimentary network security protections within the car, such as maliciously bridging between our car's two internal subnets. We also present composite attacks that leverage individual weaknesses, including an attack that embeds malicious code in a car's telematics unit and that will completely erase any evidence of its presence after a crash. Looking forward, we discuss the complex challenges in addressing these vulnerabilities while considering the existing automotive ecosystem.

/pdf/experimental-security-analysis-of-a-modern-automobile-27zp79jtxi.pdf

Experimental Security Analysis of a Modern Automobile

Recent developments in the field of cryptanalysis of hash functions has inspired NIST to announce a competition for selecting a new cryptographic hash function to join the SHA family of standards. One of the 14 second-round candidateswasCubeHash designed by Daniel J. Bernstein. CubeHash is a unique hash function in the sense that it does not iterate a common compression function, and offers a structure which resembles a sponge function, even though it is not exactly a sponge function.

In this paper we analyze reduced-round variants of CubeHash where the adversary controls the full 1024-bit input to reduced-round Cube-Hash and can observe its full output. We show that linear approximations with high biases exist in reduced-round variants. For example, we present an 11-round linear approximation with bias of 2-235, which allows distinguishing 11-round CubeHash using about 2470 queries. We also discuss the extension of this distinguisher to 12 rounds using message modification techniques. Finally, we present a linear distinguisher for 14-round CubeHash which uses about 2812 queries.

/pdf/linear-analysis-of-reduced-round-cubehash-2toslve0d5.pdf

Linear analysis of reduced-round cubehash

A continuously-observable steganographic file system allows to remotely store user files on a raw storage device; the security goal is to offer plausible deniability even when the raw storage device is continuously monitored by an attacker. Zhou, Pang and Tan have proposed such a system in [7] with a claim of provable security against traffic analysis. In this paper, we disprove their claims by presenting traffic analysis attacks on the file update algorithm of Zhou et al. Our attacks are highly effective in detecting file updates and revealing the existence and location of files. For multi-block files, we show that two updates are sufficient to discover the file. One-block files accessed a sufficient number of times can also be revealed. Our results suggest that simple randomization techniques are not sufficient to protect steganographic file systems from traffic analysis attacks.

/pdf/traffic-analysis-attacks-on-a-continuously-58traoswre.pdf

Traffic analysis attacks on a continuously-observablesteganographic file system

The ALRED family of Message Authentication Codes (MACs) is based on three principles: Using a keyless block cipher in CBC mode to process the message, choosing AES-128 as this cipher, and reducing the effective number of rounds to 4 in order to speed up the processing. In this paper we show that each one of these principles creates significant weaknesses. More specifically, we show that any ALRED-type MAC which uses a keyless block cipher is vulnerable to new time/memory tradeoff attacks which are faster than generic tradeoff attacks on one-way functions. We then use the special properties of keyless AES to attack any number of rounds (4, 10, or a million) by forging the MAC of essentially any desired message in negligible time and space after a one-time preprocessing stage requiring 2 time and negligible space. For the recommended 4-round version we show how to do the same using an improved preprocessing stage with a semi-practical time complexity of 2, which is the best one can hope for in such MAC constructions. Finally, we show that even if we replace the 4-round keyless AES by a 5-round or a 6round version with additional secret round keys we can still compute such MACs much faster than via exhaustive search.

/pdf/alred-blues-new-attacks-on-aes-based-mac-s-40gpy6xhyv.pdf

ALRED Blues: New Attacks on AES-Based MAC's.

Biometrics traits such as faces, fingerprints, and irises, are becoming prevalent in computer security applications: from authentication systems to identification systems. Given the sensitive nature of biometrics, a great deal of effort is put into protecting the biometric data after it is acquired — from secure sketch and fuzzy extractors to the use of secure multiparty computations (in protocols such as SCiFI or GSHADE). While these solutions make sure that the extracted values (e.g., binary strings or vectors) that correspond to the biometrics are kept privately and securely, their practical implementations are not optimal with respect to privacy guarantees in the process of extracting the information from the raw biometric data. This paper analyses current solutions for protected systems and discusses the existing and potential problems in the security and privacy of their feature extraction and the binarization processes. As an illustrative example, we show a PoC of an attack on a feature extraction solution from facial images, used in several protected systems, and show that it reveals information which is very close to the training image of the user. As we argue in this paper, other solutions provide privacy for the system's users but make use of external set of biometric data which is often quite large, thus facing privacy and ownership issues associated with the external set of people. The take home message of this paper is: Many of the existing “privacy preserving” solutions neglect the privacy and security aspects of the feature extraction and binarization processes. Hence, we urge future research to close this gap in the security and privacy of biometric systems.

It is All in the System's Parameters: Privacy and Security Issues in Transforming Biometric Raw Data into Binary Strings

In this paper we study the problem of recovering a secret S-box from its difference distribution table (DDT). While being an interesting theoretical problem on its own, the ability to recover the S-box from the DDT of a secret S-box can be used in cryptanalytic attacks where the attacker can obtain the DDT (e.g., in Bar-On et al.’s attack on GOST), in supporting theoretical analysis of the properties of difference distribution tables (e.g., in Boura et al.’s work), or in some analysis of S-boxes with unknown design criteria (e.g., in Biryukov and Perrin’s analysis).We show that using the well established relation between the DDT and the linear approximation table (LAT), one can devise an algorithm different from the straightforward guess-and-determine (GD) algorithm proposed by Boura et al. Moreover, we show how to exploit this relation, and embed the knowledge obtained from it in the GD algorithm. We tested our new algorithm on random S-boxes of different sizes, and for random 14-bit bijective S-boxes, our results outperform the GD attack by several orders of magnitude.

/pdf/reconstructing-an-s-box-from-its-difference-distribution-3eayxbo55i.pdf

Orr Dunkelman

Papers

Linear analysis of reduced-round cubehash

Traffic analysis attacks on a continuously-observablesteganographic file system

ALRED Blues: New Attacks on AES-Based MAC's.

It is All in the System's Parameters: Privacy and Security Issues in Transforming Biometric Raw Data into Binary Strings

Reconstructing an S-box from its Difference Distribution Table