David Derler (DFINITY), Kai Samelin (TÜV Rheinland i-sec GmbH), Daniel Slamanig (AIT Austrian Institute of Technology), Christoph Striecks (AIT Austrian Institute of Technology)

Blockchain technologies recently received a considerable amount of attention. While the initial focus was mainly on the use of blockchains in the context of cryptocurrencies such as Bitcoin, application scenarios now go far beyond this. Most blockchains have the property that once some object, e.g., a block or a transaction, has been registered to be included into the blockchain, it is persisted and there are no means to modify it again. While this is an essential feature of most blockchain scenarios, it is still often desirable---at times it may be even legally required--to allow for breaking this immutability in a controlled way.

Only recently, Ateniese et al. (EuroS&P 2017) proposed an elegant solution to this problem on the block level. Thereby, the authors replace standard hash functions with so-called chameleon-hashes (Krawczyk and Rabin, NDSS 2000). While their work seems to offer a suited solution to the problem of controlled re-writing of blockchains, their approach is too coarse-grained in that it only offers an all-or-nothing solution. We revisit this idea and introduce the novel concept of policy-based chameleon-hashes (PBCH). PBCHs generalize the notion of chameleon-hashes by giving the party computing a hash the ability to associate access policies to the generated hashes. Anyone who possesses enough privileges to satisfy the policy can then find arbitrary collisions for a given hash. We then apply this concept to transaction-level rewriting within blockchains, and thus support fine-grained and controlled modifiability of blockchain objects.

Besides modeling PBCHs, we present a generic construction of PBCHs (using a strengthened version of chameleon-hashes with ephemeral trapdoors which we also introduce), rigorously prove its security, and instantiate it with efficient building blocks. We report first implementation results.

View More Papers

NIC: Detecting Adversarial Samples with Neural Network Invariant Checking

Shiqing Ma (Purdue University), Yingqi Liu (Purdue University), Guanhong Tao (Purdue University), Wen-Chuan Lee (Purdue University), Xiangyu Zhang (Purdue University)

Read More

Profit: Detecting and Quantifying Side Channels in Networked Applications

Nicolás Rosner (University of California, Santa Barbara), Ismet Burak Kadron (University of California, Santa Barbara), Lucas Bang (Harvey Mudd College), Tevfik Bultan (University of California, Santa Barbara)

Read More

Private Continual Release of Real-Valued Data Streams

Victor Perrier (Data61, CSIRO and ISAE-SUPAERO), Hassan Jameel Asghar (Macquarie University and Data61, CSIRO), Dali Kaafar (Macquarie University and Data61, CSIRO)

Read More

CRCount: Pointer Invalidation with Reference Counting to Mitigate Use-after-free...

Jangseop Shin (Seoul National University and Inter-University Semiconductor Research Center), Donghyun Kwon (Seoul National University and Inter-University Semiconductor Research Center), Jiwon Seo (Seoul National University and Inter-University Semiconductor Research Center), Yeongpil Cho (Soongsil University), Yunheung Paek (Seoul National University and Inter-University Semiconductor Research Center)

Read More