Despite the growing adoption of blockchains, their isolated architectures hinder seamless cross-chain communication, challenging applications that rely on integrated blockchain infrastructures, notably Blockchain-based Information Systems (BISs). Achieving interoperability while preserving privacy and regulatory compliance remains a core challenge, particularly when separate organizations operate different blockchain platforms and tokenized value must move across them without exposing transaction links that may reveal business relationships or payment behavior. Existing interoperability solutions often incur high computational overhead and rely on protocol-specific assumptions, limiting their applicability across heterogeneous blockchains. We introduce zkPACT, a privacy-preserving framework for compliant cross-chain token transfers across heterogeneous blockchains. Our framework combines Zero-Knowledge Proofs (ZKPs), oracle networks, and off-chain batching to support scalable transfers. It employs a coordinated oracle model in which validators process cross-chain burn events, while a rotating aggregator updates the shared off-chain Merkle tree after reaching consensus, enabling private and efficient token claims. To improve scalability and reduce gas costs, zkPACT batches claim requests off-chain and then submits a single succinct proof to the smart contract. To ensure validator accountability, the framework enforces an incentive mechanism and dynamic slashing. We also integrate a Know Your Customer (KYC) mechanism that enables users to demonstrate compliance without revealing sensitive data, preserving privacy and accountability in the event of abuse. We present a proof-of-concept implementation of zkPACT that achieves up to 95% lower gas costs and up to 94% lower off-chain memory usage than a non-batching approach, demonstrating its suitability for private, scalable cross-chain token transfers.

Advertising campaigns are crucial in business development, but most marketing techniques target online purchases (e.g., Google Adsense) and rely on a centralized architecture to store and process the campaign's data and check its effectiveness. Recently, proximity marketing has become more popular thanks to the widespread use of smartphones. It exploits the short-range communication (e.g., Bluetooth) between smartphones and beacon devices to collect and send marketing information to customers. However, this might create privacy issues for customers due to the potential leakage of sensitive information (such as locations associated with time). In this paper, we propose ProMark, a privacy-aware blockchain-based platform to verify the effectiveness of proximity marketing campaigns by ensuring transparency, decentralization, and privacy in the measurement process. We implemented ProMark and carried out experiments that show that ProMark can be used in super-regional malls even during peak hours.

Knowledge graphs (KGs) are emerging data models allowing data providers to share data. This data sharing might bring new knowledge and collaborations, with evident benefits for providers. However, since KGs might contain sensitive information about users, it is of utmost importance to ensure KG anonymization before publishing. Recently, some proposals have addressed the problem of KGs' anonymization based on the$k$-anonymity principle. These techniques propose to anonymize the whole dataset with the same anonymization level. However, in a contest where data are collected from different users, it is crucial to consider also users' preferences on the anonymization level to adopt for their data. To cope with this requirement, this paper presents the Personalized$k$-Attribute Degree (p-$k$-ad) principle. It allows users to specify their anonymity levels (the$k$values) while preventing adversaries from re-identifying them with a confidence higher than$\frac{1}{k}$with their specified$k$. Moreover, we design the Personalized Cluster-Based Knowledge Graph Anonymization Algorithm (PCKGA) to generate anonymized KGs satisfying p-$k$-ad. We conduct experiments on four real-life datasets and show that PCKGA greatly improves the quality of anonymized KGs comparing to previous algorithms.

Knowledge graphs (KGs) are widely shared because they can model both users' attributes as well as their relationships. Unfortunately, adversaries can re-identify their victims in these KGs by using a rich background knowledge about not only the victims' attributes but also their relationships. A preliminary work to deal with this issue has been proposed in [1] which anonymizes both user attributes and relationships, but this is not enough. Indeed, adversaries can still re-identify target users if data providers publish new versions of their anonymized KGs. We remedy this problem by presenting the kw-Time-Varying Attribute Degree (kw-tad) principle that prevents adversaries from re-identifying any user appearing in w continuous anonymized KGs with a confidence higher than rac{1}{k}. Moreover, we introduce the Cluster-based Time-Varying Knowledge Graph Anonymization Algorithm to generate anonymized KGs satisfying kw-tad. Finally, we prove that even if data providers insert/re-insert/update/delete their users, the users are protected by kw-tad.