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

Time Does Not Heal All Wounds: A Longitudinal Analysis...

Meng Luo (Stony Brook University), Pierre Laperdrix (Stony Brook University), Nima Honarmand (Stony Brook University), Nick Nikiforakis (Stony Brook University)

Read More

Neural Machine Translation Inspired Binary Code Similarity Comparison beyond...

Fei Zuo (University of South Carolina), Xiaopeng Li (University of South Carolina), Patrick Young (Temple University), Lannan Luo (University of South Carolina), Qiang Zeng (University of South Carolina), Zhexin Zhang (University of South Carolina)

Read More

Statistical Privacy for Streaming Traffic

Xiaokuan Zhang (The Ohio State University), Jihun Hamm (The Ohio State University), Michael K. Reiter (University of North Carolina at Chapel Hill), Yinqian Zhang (The Ohio State University)

Read More

Privacy Attacks to the 4G and 5G Cellular Paging...

Syed Rafiul Hussain (Purdue University), Mitziu Echeverria (University of Iowa), Omar Chowdhury (University of Iowa), Ninghui Li (Purdue University), Elisa Bertino (Purdue University)

Read More