It is a common narrative that blockchains are immutable and so it is technically impossible to erase data stored on them. For legal and ethical reasons, however, individuals and organizations might be compelled to erase locally stored data, be it encoded on a blockchain or not. The common assumption for blockchain networks like Bitcoin is that forcing nodes to erase data contained on the blockchain is equal to permanently restricting them from participating in the system in a full-node role. Challenging this belief, in this paper, we propose and demonstrate a pragmatic approach towards functionality-preserving local erasure (FPLE). FPLE enables full nodes to erase infringing or undesirable data while continuing to store and validate most of the blockchain. We describe a general FPLE approach for UTXO-based (i.e., Bitcoin-like) cryptocurrencies and present a lightweight proof-of-concept tool for safely erasing transaction data from the local storage of Bitcoin Core nodes. Erasing nodes continue to operate in tune with the network even when erased transaction outputs become relevant for validating subsequent blocks. Using only our basic proof-of-concept implementation, we are already able to safely comply with a significantly larger range of erasure requests than, to the best of our knowledge, any other full node operator so far.
When considering data protection as a reason for erasure (cf. sub:data_protection ), it also noteworthy that a large body of works deal with the challenge of providing to blockchain users (see e. ,g. @cite_24 for a recent survey). However, most transactions in popular systems like Bitcoin do not use any additional means of increasing anonymity @cite_23 and are reidentifiable using well-known techniques @cite_24 . Even when strong privacy guarantees can be achieved through technical means, this provides no solution for cases where identifiable data is posted to the blockchain on purpose, e. ,g., as part of doxing.
@cite_24 @cite_23
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This paper contributes a systematic account of transaction anonymization techniques that do not require trust in a single entity and support the existing cryptographic currency Bitcoin. It surveys and compares four known techniques, proposes tailored metrics to identify the use of each technique (but not necessarily its users), and presents longitudinal measurements indicating adoption trends and teething troubles. There is a tradeoff between the choice of users' preferred protection mechanisms and the risk that pertaining transactions can be singled out, which hurts privacy due to smaller anonymity sets unless a critical mass adopts the mechanism. Since the introduction of Bitcoin in 2008, blockchain systems have seen an enormous increase in adoption. By providing a persistent, distributed, and append-only ledger, blockchains enable numerous applications such as distributed consensus, robustness against equivocation, and smart contracts. However, recent studies show that blockchain systems such as Bitcoin can be (mis) used to store arbitrary content. This has already been used to store arguably objectionable content on Bitcoin's blockchain. Already single instances of clearly objectionable or even illegal content can put the whole system at risk by making its node operators culpable. To overcome this imminent risk, we survey and discuss the design space of countermeasures against the insertion of such objectionable content. Our analysis shows a wide spectrum of potential countermeasures, which are often combinable for increased efficiency. First, we investigate special-purpose content detectors as an ad hoc mitigation. As they turn out to be easily evadable, we also investigate content-agnostic countermeasures. We find that mandatory minimum fees as well as mitigation of transaction manipulability via identifier commitments significantly raise the bar for inserting harmful content into a blockchain. We propose a privacy protection framework for large-scale content-based information retrieval. It offers two layers of protection. First, robust hash values are used as queries to prevent revealing original content or features. Second, the client can choose to omit certain bits in a hash value to further increase the ambiguity for the server. Due to the reduced information, it is computationally difficult for the server to know the client’s interest. The server has to return the hash values of all possible candidates to the client. The client performs a search within the candidate list to find the best match. Since only hash values are exchanged between the client and the server, the privacy of both parties is protected. We introduce the concept oftunable privacy, where the privacy protection level can be adjusted according to a policy. It is realized through hash-based piecewise inverted indexing. The idea is to divide a feature vector into pieces and index each piece with a subhash value. Each subhash value is associated with an inverted index list. The framework has been extensively tested using a large image database. We have evaluated both retrieval performance and privacy-preserving performance for a particular content identification application. Two different constructions of robust hash algorithms are used. One is based on random projections; the other is based on the discrete wavelet transform. Both algorithms exhibit satisfactory performance in comparison with state-of-the-art retrieval schemes. The results show that the privacy enhancement slightly improves the retrieval performance. We consider the majority voting attack for estimating the query category and identification. Experiment results show that this attack is a threat when there are near-duplicates, but the success rate decreases with the number of omitted bits and the number of distinct items. Several applications require robust and tamper-proof logging systems, e.g. electronic voting or bank information systems. At Scytl we use a technology, called immutable logs, that we deploy in our electronic voting solutions. This technology ensures the integrity, authenticity and non-repudiation of the generated logs, thus in case of any event the auditors can use them to investigate the issue. As a security recommendation it is advisable to store and or replicate the information logged in a location where the logger has no writing or modification permissions. Otherwise, if the logger gets compromised, the data previously generated could be truncated or altered using the same private keys. This approach is costly and does not protect against collusion between the logger and the entities that hold the replicated data. In order to tackle these issues, in this article we present a proposal and implementation to immutabilize integrity proofs of the secure logs within the Bitcoin’s blockchain. Due to the properties of the proposal, the integrity of the immutabilized logs is guaranteed without performing log data replication and even in case the logger gets latterly compromised.