FAIR: Forwarding Accountability for Internet Reputability

This paper presents FAIR, a forwarding accountability mechanism that incentivizes ISPs to apply stricter security policies to their customers. The Autonomous System (AS) of the receiver specifies a traffic profile that the sender AS must adhere to. Transit ASes on the path mark packets. In case of traffic profile violations, the marked packets are used as a proof of misbehavior. FAIR introduces low bandwidth overhead and requires no per-packet and no per-flow state for forwarding. We describe integration with IP and demonstrate a software switch running on commodity hardware that can switch packets at a line rate of 120 Gbps, and can forward 140M minimum-sized packets per second, limited by the hardware I/O subsystem. Moreover, this paper proposes a "suspicious bit" for packet headers - an application that builds on top of FAIR's proofs of misbehavior and flags packets to warn other entities in the network.Comment: 16 pages, 12 figure

Generierung von dendritischen Zellen bei Patienten mit einem Multiplen Myelom

Zusammenfassend gelang es in der vorliegenden Arbeit reife dendritische Zellen aus Blutmonozyten von Patienten mit Multiplem Myelom zu generieren. Die Fähigkeit zur Antigenpräsentation und Aktivierung von T-Lymphozyten durch die dendritischen Zellen konnte nachgewiesen werden

The monoidal structure on strict polynomial functors

Reischuk R. The monoidal structure on strict polynomial functors. Bielefeld: Universität Bielefeld; 2016

ADSNARK: Nearly practical and privacy-preserving proofs on authenticated data

We study the problem of privacy-preserving proofs on authenticated data, where a party receives data from a trusted source and is requested to prove computations over the data to third parties in a correct and private way, i.e., the third party learns no information on the data but is still assured that the claimed proof is valid. Our work particularly focuses on the challenging requirement that the third party should be able to verify the validity with respect to the specific data authenticated by the source — even without having access to that source. This problem is motivated by various scenarios emerging from several application areas such as wearable computing, smart metering, or general business-to-business interactions. Furthermore, these applications also demand any meaningful solution to satisfy additional properties related to usability and scalability. In this paper, we formalize the above three-party model, discuss concrete application scenarios, and then we design, build, and evaluate ADSNARK, a nearly practical system for proving arbitrary computations over authenticated data in a privacy-preserving manner. ADSNARK improves significantly over state-of-the-art solutions for this model. For instance, compared to corresponding solutions based on Pinocchio (Oakland’13), ADSNARK achieves up to 25× improvement in proof-computation time and a 20× reduction in prover storage space

Two nonlinear lower bounds for on-line computations

The following lower bounds for on-line computation are proved: (1) Simulating two-tape nondeterministic machines by one-tape machines requires Ω(n log n) time. (2) Simulating k-tape (deterministic) machines by machines with k-pushdown stores requires Ω(n log1/(k+1)n) time