The Interplanetary File System (IPFS) [1] is a popular Peer-to-Peer (P2P) overlay network with a focus on content-addressed data exchange. While IPFS offers decentralized file storage, improving resilience, it suffers from privacy challenges [3]. In particular Bitswap, the data exchange protocol suffers under these challenges. That is, Bitswap contacts all neighbors for content discovery revealing interest to many participants.

Bitswap is the data exchange protocol for the content-addressed peer-to-peer overlay network IPFS. During content discovery, Bitswap reveals the interest of a peer in content to all neighbors, enabling the tracking of user interests. In our paper, we propose a modification of the Bitswap protocol, which enables source obfuscation using proxies for content discovery. The proxies are selected via a random-walk. Enabling content discovery through proxies introduces plausible deniability. We evaluate the protocol modification with a simulation. The protocol modification demonstrates enhanced privacy, while maintaining acceptable performance levels.

IPFS is a content-addressed peer-to-peer data network, which follows the paradigm of information centric networking. In IPFS, data is exchanged with the Bitswap protocol. For content discovery, Bitswap queries all neighbors for the content, leaking the interest to all neighbors. In our paper, we develop three privacy-enhanced protocols for content discovery, which reduce the interest leak from all neighbors to ideally one content provider. Our protocols use probabilistic data structures like Bloom filter and cryptographic approaches like Private Set Intersection. We implement our protocols as proof of concept and show how they can be integrated into the go implementation of Bitswap. Furthermore, we provide a measurement supported performance, load, and privacy evaluation of the three protocols, showing their feasibility trade-offs.

IPFS is a content-addressed decentralized peer-to-peer data network, using the Bitswap protocol for exchanging data. The data exchange leaks the information to all neighbors, compromising a user’s privacy. This paper investigates the suitability of forwarding with source obfuscation techniques for improving the privacy of the Bitswap protocol. The usage of forwarding can add plausible deniability and the source obfuscation provides additional protection against passive observers. First results showed that through trickle-spreading the source prediction could decrease to 40%, at the cost of an increased content fetching time. However, assuming short distances between content provider and consumer the content fetching time can be faster even with the additional source obfuscation.

Recommender systems are widely used. Usually, recommender systems are based on a centralized client-server architecture. However, this approach implies drawbacks regarding the privacy of users. In this paper, we propose a distributed reciprocal recommender system with strong, self-determined privacy guarantees, i.e., local differential privacy. More precisely, users randomize their profiles locally and exchange them via a peer-to-peer network. Recommendations are then computed and ranked locally by estimating similarities between profiles. We evaluate recommendation accuracy of a job recommender system and demonstrate that our method provides acceptable utility under strong privacy requirements.

Due to their immutable log of information, blockchains can be considered as a transparency-enhancing technology. The immutability, however, also introduces threats and challenges with respect to privacy laws and illegal content. Introducing a certain degree of mutability, which enables the possibility to store and remove information, can therefore increase the opportunities for blockchains. In this paper, we present a concept for a mutable blockchain structure. Our approach enables the removal of certain blocks, while maintaining the blockchain's verifiability property. Since our concept is agnostic to any consensus algorithms, it can be implemented with permissioned and permissionless blockchains.

Zcash is a privacy-preserving cryptocurrency that provides anonymous monetary transactions. While Zcash's anonymity is part of a rigorous scientific discussion, information on the underlying peer-to-peer network are missing. In this paper, we provide the first long-term measurement study of the Zcash network to capture key metrics such as the network size and node distribution as well as deeper insights on the centralization of the network. Furthermore, we present an inference method based on a timing analysis of block arrivals that we use to determine interconnections of nodes. We evaluate and verify our method through simulations and real-world experiments, yielding a precision of 50% with a recall of 82% in the real-world scenario. By adjusting the parameters, the topology inference model is adaptable to the conditions found in other cryptocurrencies and therefore also contributes to the broader discussion of topology hiding in general.