Prelude: Ensuring Inter-Domain Loop-Freedom in~SDN-Enabled Networks

Software-Defined-eXchanges (SDXes) promise to tackle the timely quest of bringing improving the inter-domain routing ecosystem through SDN deployment. Yet, the naive deployment of SDN on the Internet raises concerns about the correctness of the inter-domain data-plane. By allowing operators to deflect traffic from the default BGP route, SDN policies are susceptible of creating permanent forwarding loops invisible to the control-plane. In this paper, we propose a system, called Prelude, for detecting SDN-induced forwarding loops between SDXes with high accuracy without leaking the private routing information of network operators. To achieve this, we leverage Secure Multi-Party Computation (SMPC) techniques to build a novel and general privacy-preserving primitive that detects whether any subset of SDN rules might affect the same portion of traffic without learning anything about those rules. We then leverage that primitive as the main building block of a distributed system tailored to detect forwarding loops among any set of SDXes. We leverage the particular nature of SDXes to further improve the efficiency of our SMPC solution. The number of valid SDN rules, i.e., not creating loops, rejected by our solution is 100x lower than previous privacy-preserving solutions, and also provides better privacy guarantees. Furthermore, our solution naturally provides network operators with some hindsight on the cost of the deflected paths

Towards Practical Privacy-Preserving Protocols

Protecting users' privacy in digital systems becomes more complex and challenging over time, as the amount of stored and exchanged data grows steadily and systems become increasingly involved and connected. Two techniques that try to approach this issue are Secure Multi-Party Computation (MPC) and Private Information Retrieval (PIR), which aim to enable practical computation while simultaneously keeping sensitive data private. In this thesis we present results showing how real-world applications can be executed in a privacy-preserving way. This is not only desired by users of such applications, but since 2018 also based on a strong legal foundation with the General Data Protection Regulation (GDPR) in the European Union, that forces companies to protect the privacy of user data by design. This thesis' contributions are split into three parts and can be summarized as follows: MPC Tools Generic MPC requires in-depth background knowledge about a complex research field. To approach this, we provide tools that are efficient and usable at the same time, and serve as a foundation for follow-up work as they allow cryptographers, researchers and developers to implement, test and deploy MPC applications. We provide an implementation framework that abstracts from the underlying protocols, optimized building blocks generated from hardware synthesis tools, and allow the direct processing of Hardware Definition Languages (HDLs). Finally, we present an automated compiler for efficient hybrid protocols from ANSI C. MPC Applications MPC was for a long time deemed too expensive to be used in practice. We show several use cases of real-world applications that can operate in a privacy-preserving, yet practical way when engineered properly and built on top of suitable MPC protocols. Use cases presented in this thesis are from the domain of route computation using BGP on the Internet or at Internet Exchange Points (IXPs). In both cases our protocols protect sensitive business information that is used to determine routing decisions. Another use case focuses on genomics, which is particularly critical as the human genome is connected to everyone during their entire lifespan and cannot be altered. Our system enables federated genomic databases, where several institutions can privately outsource their genome data and where research institutes can query this data in a privacy-preserving manner. PIR and Applications Privately retrieving data from a database is a crucial requirement for user privacy and metadata protection, and is enabled amongst others by a technique called Private Information Retrieval (PIR). We present improvements and a generalization of a well-known multi-server PIR scheme of Chor et al., and an implementation and evaluation thereof. We also design and implement an efficient anonymous messaging system built on top of PIR. Furthermore we provide a scalable solution for private contact discovery that utilizes ideas from efficient two-server PIR built from Distributed Point Functions (DPFs) in combination with Private Set Intersection (PSI)

Implementation of a Secure Multiparty Computation Protocol

Secure multiparty computation (SMC) allows a set of parties to jointly compute a function on private inputs such that, they learn only the output of the function, and the correctness of the output is guaranteed even when a subset of the parties is controlled by an adversary. SMC allows data to be kept in an uncompromisable form and still be useful, and it also gives new meaning to data ownership, allowing data to be shared in a useful way while retaining its privacy. Thus, applications of SMC hold promise for addressing some of the security issues information-driven societies struggle with. In this thesis, we implement two SMC protocols. Our primary objective is to gain a solid understanding of the basic concepts related to SMC. We present a brief survey of the field, with focus on SMC based on secret sharing. In addition to the protocol im- plementations, we implement circuit randomization, a common technique for efficiency improvement. The implemented protocols are run on a simulator to securely evaluate some simple arithmetic functions, and the round complexities of the implemented protocols are compared. Finally, we attempt to extend the implementation to support more general computations

Privacy-Preserving Interdomain Routing at Internet Scale

The Border Gateway Protocol (BGP) computes routes between the organizational networks that make up today\u27s Internet. Unfortunately, BGP suffers from deficiencies, including slow convergence, security problems, a lack of innovation, and the leakage of sensitive information about domains\u27 routing preferences. To overcome some of these problems, we revisit the idea of centralizing and using secure multi-party computation (MPC) for interdomain routing which was proposed by Gupta et al. (ACM HotNets\u2712). We implement two algorithms for interdomain routing with state-of-the-art MPC protocols. On an empirically derived dataset that approximates the topology of today\u27s Internet (55,809 nodes), our protocols take as little as 6 s of topology-independent precomputation and only 3s of online time. We show, moreover, that when our MPC approach is applied at country/region-level scale, runtimes can be as low as 0.17 s online time and 0.20 s pre-computation time. Our results motivate the MPC approach for interdomain routing and furthermore demonstrate that current MPC techniques are capable of efficiently tackling real-world problems at a large scale