Decentralised learning has recently gained traction as an alternative to federated learning in which both data and coordination are distributed over its users. To preserve the confidentiality of users' data, decentralised learning relies on differential privacy, multi-party computation, or a combination thereof. However, running multiple privacy-preserving summations in sequence may allow adversaries to perform reconstruction attacks. Unfortunately, current reconstruction countermeasures either cannot trivially be adapted to the distributed setting, or add excessive amounts of noise.

In this work, we first show that passive honest-but-curious adversaries can infer other users' private data after several privacy-preserving summations. For example, in subgraphs with 18 users, we show that only three passive honest-but-curious adversaries succeed at reconstructing private data 11.0% of the time, requiring an average of 8.8 summations per adversary. The success rate depends only on the adversaries' direct neighbourhood, and is independent of the size of the full network. We consider weak adversaries that do not control the graph topology, cannot exploit the inner workings of the summation protocol, and do not have auxiliary knowledge; and show that these adversaries can still infer private data.

We develop a mathematical understanding of how reconstruction relates to topology and propose the first topology-based decentralised defence against reconstruction attacks. Specifically, we show that reconstruction requires a number of adversaries linear in the length of the network's shortest cycle. Consequently, exact reconstruction attacks over privacy-preserving summations are impossible in acyclic networks.

Our work is a stepping stone for a formal theory of topology-based decentralised reconstruction defences. Such a theory would generalise our countermeasure beyond summation, define confidentiality in terms of entropy, and describe the interactions with (topology-aware) differential privacy.@en

Private set intersection protocols allow two parties with private sets of data to compute the intersection between them without leaking other information about their sets. These protocols have been studied for almost 20 years, and have been significantly improved over time, reducing both their computation and communication costs. However, when more than two parties want to compute a private set intersection, these protocols are no longer applicable. While extensions exist to the multi-party case, these protocols are significantly less efficient than the two-party case. It remains an open question to design collusion-resistant multi-party private set intersection (MPSI) protocols that come close to the efficiency of two-party protocols. This work is made more difficult by the immense variety in the proposed schemes and the lack of systematization. Moreover, each new work only considers a small subset of previously proposed protocols, leaving out important developments from older works. Finally, MPSI protocols rely on many possible constructions and building blocks that have not been summarized. This work aims to point protocol designers to gaps in research and promising directions, pointing out common security flaws and sketching a frame of reference. To this end, we focus on the semi-honest model. We conclude that current MPSI protocols are not a one-size-fits-all solution, and instead there exist many protocols that each prevail in their own application setting.

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We consider the problem of publicly verifiable privacy-preserving data aggregation in the presence of a malicious aggregator colluding with malicious users. State-of-the-art solutions either split the aggregator into two parties under the assumption that they do not collude, or require many rounds of interactivity and have non-constant verification time. In this work, we propose mPVAS, the first publicly verifiable privacy-preserving data aggregation protocol that allows arbitrary collusion, without relying on trusted third parties during execution, where verification runs in constant time. We also show three extensions to mPVAS: mPVAS+, for improved communication complexity, mPVAS-IV, for the identification of malicious users, and mPVAS-UD, for graceful handling of reduced user availability without the need to redo the setup. We show that our schemes achieve the desired confidentiality, integrity, and authenticity. Finally, through both theoretical and experimental evaluations, we show that our schemes are feasible for real-world applications.

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Blockchain’s potential to revolutionize supply chain and logistics with transparency and equitable stakeholder engagement is significant. However, challenges like scalability, privacy, and interoperability persist. This study explores the scarcity of real-world blockchain implementations in supply chain and logistics since we have not witnessed many real-world deployments of blockchain-based solutions in the field. Puzzled by this, we integrate technology, user experience, and operational efficiency to illuminate the complex landscape of blockchain integration. We present blockchain-based solutions in three use cases, comparing them with alternative designs and analyzing them in terms of technical, economic, and operational aspects. Insights from a tailored questionnaire of 50 questions addressed to practitioners and experts offer crucial perspectives on blockchain adoption. One of the key findings from our work shows that half of the companies interviewed agree that they will miss the potential for competitive advantage if they do not invest in blockchain technology, and 61% of the companies surveyed claimed that their customers ask for more transparency in supply chain-related transactions. However, only one-third of the companies were aware of the main features of blockchain technology, which shows a lack of knowledge among the companies that may lead to a weaker blockchain adaption in supply chain use cases. Our readers should note that our study is specifically contextualized in a Netherlands-funded national project. We hope that researchers as well as stakeholders in supply chain and logistics can benefit from the insights of our work.@en

In the past decade, tens of homomorphic encryption compilers have been released, and there are good reasons for these compilers to exist. Firstly, homomorphic encryption is a powerful secure computation technique in that it is relatively easy for parties to switch from plaintext computation to secure computations when compared to techniques like secret sharing. However, the technique is mathematically involved and requires expert knowledge to express computations as homomorphic encryption operations. So, these compilers support users who might otherwise not have the time or expertise to optimize the computation manually. Another reason is that homomorphic encryption is still computationally expensive, so compilers allow users to optimize their secure computation tasks. One major shortcoming of these compilers is that they often do not allow users to use high-level primitives, such as equality checks, comparisons, and AND and OR operations between many operands. The compilers that do are either based on TFHE, requiring large bootstrapping keys that must be sent to the evaluator, or they only work in the Boolean domain, excluding many potentially more performant circuits. Moreover, compilers must reduce the multiplicative depth of the circuits they generate to minimize the noise growth inherent to these homomorphic encryption schemes. However, many compilers only consider reducing the depth as an afterthought. We propose the Oraqle compiler, which solves both problems at once by implementing depth-aware arithmetization, a technique for expressing high-level primitives as arithmetic operations that are executable by homomorphic encryption libraries. Instead of generating one possible circuit, the compiler generates multiple circuits that trade off the number of multiplications with the multiplicative depth. If the depth of the resulting circuits is low enough, they may be evaluated using a BFV or BGV library that does not require bootstrapping keys. We demonstrate that our compiler allows for significant performance gains.@en

Medical research benefits from large quantities of high-quality data. Internet-based data-sharing platforms bring the advantage of rapidly sharing data medical data. However, ensuring security and accountability in networked medical systems remains a challenge. In this paper, we propose a secure and auditable data-sharing platform for hospitals and research groups based on a distributed ledger. A two-party protocol for recoverable key agreement lies at the basis of securing the data sharing. This protocol enables two parties to agree on an encryption key and put the encryption key under the escrow of a board of semi-trusted auditors. A quorum of these auditors is required in order to recover the encryption key. The recoverable key agreement ensures that past communication can be audited, even if one of the two parties is malicious. We provide a realization of the protocol and analyze its complexity and performance. Based on these analyses, we demonstrate that the protocol is suitable for real-world use cases and resource-constrained devices.

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With the fast development of e-commerce, there is a higher demand for timely delivery. Logistic companies want to send receivers a more accurate arrival prediction to improve customer satisfaction and lower customer retention costs. One approach is to share (near) real-time location data with recipients, but this also introduces privacy and security issues such as malicious tracking and theft. In this paper, we propose a privacy-preserving real-time location sharing system including (1) a differential privacy based location publishing method and (2) location sharing protocols for both centralized and decentralized platforms. Different from existing location perturbation solutions which only consider privacy in theory, our location publishing method is based on a real map and different privacy levels for recipients. Our analyses and proofs show that the proposed location publishing method provides better privacy protection than existing works under real maps against possible attacks. We also provide a detailed analysis of the choice of the privacy parameter and their impact on the suggested noisy location outputs. The experimental results demonstrate that our proposed method is feasible for both centralized and decentralized systems and can provide more precise arrival prediction than using time slots in current delivery systems.

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Employing blockchain and privacy-enhancing technologies, MedTech Chain promises an authenticated, decentralised, secure, and privacy-preserving environment for the real-time research and monitoring of medical device data. Through its querying functionalities, the platform can provide valuable insights for threat intelligence, medical research and hospital management. To our knowledge, the approach is among the first to employ ϵ-differential privacy in the context of medical device data. The current work details the framework's functionality and demonstrates a negligible time overhead induced by ϵ-differential privacy to data analysis.

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Verifiable Credential (VC) is a new standard proposed by the W3C association to facilitate the expression and verification of third-party-verified credentials on the Internet, such as passports or diplomas. However, the current VC data model lacks an explicit revocation design that guarantees the secure operations of the system, which limits its application. In this paper, we specify the requirements for a tamper-evident and privacy-preserving revocation mechanism, based on which we compare existing solutions and propose our revocation mechanism that satisfies all the requirements. Our design combines a cryptographic accumulator and a role-based blockchain. With zero-knowledge proof, the verifier can operate off-chain computation of the revocation status while ensuring the correctness of revocation information published on the blockchain. Our analysis shows that the proposed revocation mechanism can prevent fraud using forged and revoked credentials and relieve privacy concerns caused by the correlation of digital data. Our proof-of-concept implementation demonstrates that our revocation mechanism adds only 42.86 ms overhead in the presentation and 31.36 ms overhead in the verification of verifiable credentials. We also provide scalability analysis, which illustrates that the throughput of our blockchain can meet real-world needs.@en

Double auctions are procedures to trade commodities such as electricity or parts of the wireless spectrum at optimal prices. Buyers and sellers inform the auctioneer what quantity they want to buy or sell at specific prices. The auctioneer aggregates these offers into demand and supply curves and finds the intersection representing the optimal price. In this way, commodities exchange owners in an economically-efficient manner. Ideally, the auctioneer is a trusted third party that does not abuse the information they gain. However, the offers reveal sensitive information about the traders, which the auctioneer may use for economic gain as insider information. These concerns are not theoretical; investigations against auctioneers in electricity and advertisement auctions for manipulating auctions are ongoing. These concerns call for solutions that conduct double auctions in a privacy-preserving and verifiable way. However, current solutions are impractical: To the best of our knowledge, the only solutions satisfying these properties require full interaction of all participants. In this work, we design a more practical solution. We propose the first privacy-preserving and verifiable double auction scheme that does not require traders to interact actively, tailored to electricity trading on (inter)national exchanges. Our solution relies on homomorphic encryption, commitments, and zero-knowledge proofs. In a simulated auction with 256 traders, we observe that traders take up to 10 seconds to generate their order, the auctioneer takes 10 seconds to verify an order, and the auction result is computed and verified in 30 seconds. We extrapolate these results to larger auctions to show the practical potential.

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In January 2017, a truck crossed the border between Spain and France for the first time using an e-CMR: An electronic version of the primary transport document required for inter-European logistics. Since that crossing, researchers and logistic organizations have proposed a large number of ideas to further digitize Europe’s supply chain. Many of these ideas involve blockchains, but not all of them validate the data that is posted to them. As a result, participants can make illegitimate claims: Even though the blockchain enables transparency and immutability of the data stores, it does not ensure veracity. We provide several examples of works about information sharing in the supply chain that do not perform such validation. One work that does use the blockchain’s validation functionality is DEFEND. DEFEND addresses customs agencies’ lack of information for international freight inspection by tracking shipping containers throughout their journey. As containers pass from one operator to another, the blockchain participants ensure that containers are not doubly spent. In this work, we propose an extension of DEFEND, in which we further extend the capabilities for validation. Moreover, we provide actual cryptographic protocols to preserve participants’ privacy while DEFEND only described privacy on a high level. Finally, by making a more fine-grained distinction between different actors in the chain, we model the entire supply chain from buyer to seller. As a result, the buyer and seller can now track the respective package’s whereabouts through each leg of its journey.@en

Cloud services are an essential part of our digital infrastructure as organizations outsource large amounts of data storage and computations. While organizations typically keep sensitive data in encrypted form at rest, they decrypt it when performing computations, leaving the cloud provider free to observe the data. Unfortunately, access to raw data creates privacy risks. To alleviate these risks, researchers have developed secure outsourced data processing techniques. Such techniques enable cloud services that keep sensitive data encrypted, even during computations. For this purpose, fully homomorphic encryption is particularly promising, but operations on ciphertexts are computationally demanding. Therefore, modern fully homomorphic cryptosystems use packing techniques to store and process multiple values within a single ciphertext. However, a problem arises when packed data in one ciphertext does not align with another. For this reason, we propose a method to construct circuits that perform arbitrary permutations and mappings of such packed values. Unlike existing work, our method supports moving values across multiple ciphertexts, considering that the values in real-world scenarios cannot all be packed within a single ciphertext. We compare our open-source implementation against the state-of-the-art method implemented in HElib, which we adjusted to work with multiple ciphertexts. When data is spread among five or more ciphertexts, our method outperforms the existing method by more than an order of magnitude. Even when we only consider a permutation within a single ciphertext, our method still outperforms the state-of-the-art works implemented by HElib for circuits of similar depth.

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With the emerging of e-commerce, package theft is at a high level: It is reported that 1.7 million packages are stolen or lost every day in the U.S. in 2020, which costs $25 million every day for the lost packages and the service. Information leakage during transportation is an important reason for theft since thieves can identify which truck is the target that contains the valuable products. In this paper, we address the privacy and security issues in bin-packing, which is an algorithm used in delivery centers to determine which packages should be loaded together to a certain truck. Data such as the weight of the packages is needed when assigning items into trucks, which can be called bins. However, the information is sensitive and can be used to identify the contents in the package. To provide security and privacy during bin-packing, we propose two different privacy-preserving data publishing methods. Both approaches use differential privacy (DP) to hide the existence of any specific package to prevent it from being identified by malicious users. The first approach combines differential privacy with k-anonymity, and the other one applies clustering before differential privacy. Our extensive analyses and experimental results clearly show that our proposed approaches have better privacy guarantees, better efficiency, and better performance than the existing works that use either differential privacy or k-anonymity.@en

There is an increase in interest and necessity for an interoperable and efficient railway network across Europe, creating a key distribution problem between train and trackside entities’ key management centres (KMC). Train and trackside entities establish a secure session using symmetric keys (KMAC) loaded beforehand by their respective KMC using procedures that are not scalable and prone to operational mistakes. A single system would simplify the KMAC distribution between KMCs; nevertheless, it is difficult to place the responsibility for such a system for the whole European area within one central organization. A single system could also expose relationships between KMCs, revealing information, such as plans to use an alternative route or serve a new region, jeopardizing competitive advantage. This paper proposes a scalable and decentralised key management system that allows KMC to share cryptographic keys using transactions while keeping relationships anonymous. Using non-interactive proofs of knowledge and assigning each entity a private and public key, private key owners can issue valid transactions while all system actors can validate them. Our performance analysis shows that the proposed system is scalable when a proof of concept is implemented with settings close to the expected railway landscape in 2030.@en

Privacy-preserving techniques for processing sets of information have attracted the research community's attention in recent years due to society's increasing dependency on the availability of data at any time. One of the fundamental problems in set operations is known as Private Set Intersection (PSI). The problem requires two parties to compute the intersection between their sets while preserving correctness and privacy. Although several efficient two-party PSI protocols already exist, protocols for PSI in the multi-party setting (MPSI) currently scale poorly with a growing number of parties, even though this applies to many real-life scenarios. This paper fills this gap by proposing two multi-party protocols based on Bloom filters and threshold homomorphic PKEs, which are secure in the semi-honest model. The first protocol is a multi-party PSI, whereas the second provides a more subtle functionality -threshold multi-party PSI (T-MPSI) - which outputs items of the server that appear in at least some number of other private sets. The protocols are inspired by the Davidson-Cid protocol based on Bloom filters. We compare our MPSI protocol against Kolesnikov et al., which is among the fastest known MPSI protocols. Our MPSI protocol performs better than Kolesnikov et al. in terms of run time, given that the sets are small and there is a large number of parties. Our T-MPSI protocol performs better than other existing works: the computational and communication complexities are linear in the number of elements in the largest set given a fixed number of colluding parties. We conclude that our MPSI and T-MPSI protocols are practical solutions suitable for emerging use-case scenarios with many parties, where previous solutions did not scale well.@en

Privacy-preserving data aggregation protocols have been researched widely, but usually cannot guarantee correctness of the aggregate if users are malicious. These protocols can be extended with zero-knowledge proofs and commitments to work in the malicious model, but this incurs a significant computational cost on the end users, making adoption of these protocols less likely.

We propose a privacy-preserving data aggregation protocol for calculating the sum of user inputs. Our protocol gives the aggregator confidence that all inputs are within a desired range. Instead of zero-knowledge proofs, our protocol relies on a probabilistic hypergraph-based detection algorithm with which the aggregator can quickly pinpoint malicious users. Furthermore, our protocol is robust to user dropouts and, apart from the setup phase, it is non-interactive.@en

Smart contracts can be used for the fair exchange of digital goods. A smart contract can escrow the exchange where the receiver deposits the payment, and the sender claims it by providing the goods. In the case of misbehavior, the parties provide proof on whether the received goods match the pre-agreed description or not. In general, the description is assumed to be the hash of the goods, and it is publicly known. However, without trusting the description provided by the sender, this assumption is not plausible for the scenarios where the goods are uniquely created for a specific receiver. To overcome the trust issue, sampling-based exchange protocols have been introduced where the parties use a sample of the goods as the description. Yet, the existing sampling-based proposals suffer from high on- and off-chain computational and storage costs. In this paper, we present FairDEx: an efficient sampling- based protocol that is suitable for the exchange of unique goods. Our description protocol allows us to achieve low on- and off-chain costs, which are independent of the size of the goods. The off-chain part of the protocol only utilizes highly efficient algorithms, namely hashing and symmetric key encryption. To illustrate the feasibility of FairDEx, we evaluate a research prototype on the Ethereum test network. Our results show that the cost of running FairDEx is around 0.6M gas for reasonably large sample sets, which is 30% cheaper than the state-of-the-art Ethereum-based proposals.@en

Multi-Party Private Set Intersection (MPSI) is an attractive topic in research since a practical MPSI protocol can be deployed in several real-world scenarios, including but not limited to finding the common list of customers among several companies or privacy-preserving analyses of data from different stakeholders. Several solutions have been proposed in the literature however, the existing solutions still suffer from performance related challenges such as long run-time and high bandwidth demand, particularly when the number of involved parties grows. In this paper, we propose a new approach based on threshold additively homomorphic encryption scheme, e.g., Paillier, which enables us to process the bit-set representation of sets under encryption. By doing so, it is feasible to securely compute the intersection of several data sets in an efficient manner. To prove our claims on performance, we compare the communication complexity of our approach with the existing solutions and show performance test results. We also show how the proposed protocol can be extended to securely compute other set operations on multi-party data sets.

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Large availability of smart devices and an increased number of online activities result in extensive personalized or customized services in many domains. However, the data these services mostly rely on are highly privacy-sensitive, as in pace-makers. In the last decades, many privacy breaches have increased privacy awareness, leading to stricter regulations on data processing. To comply with this legislation, proper privacy preservation mechanisms are required. One of the technological solutions, which is also provably secure, is Secure Multi-Party Computation (SMPC) that can compute any function with secret inputs. Mainly, in several SMPC solutions, such as data aggregation, we observe that secret values distributed among parties are masked with random numbers, encrypted and combined to yield the desired outcome. To ensure correct decryption of the final result, it is required that these numbers sum to a publicly known value, for instance, zero. Despite its importance, many of the corresponding works omit how to obtain such random numbers jointly or suggest procedures with high computational and communication overhead. This paper proposes two novel protocols for Joint Random Number Generation with very low computational and communication overhead. Our protocols are stand-alone and not embedded in others, and can therefore be used in data aggregation and other applications, for instance, machine learning algorithms, that require such random numbers. We first propose a protocol that relies on bit-wise sharing of individually generated random numbers, allowing parties to adapt random numbers to yield a public sum. Second, we propose a protocol that uses the sign function to generate a random number from broadcast numbers. We provide security and complexity analyses of our protocols.

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In their pursuit to maximize their return on investment, cybercriminals will likely reuse as much as possible between their campaigns. Not only will the same phishing mail be sent to tens of thousands of targets, but reuse of the tools and infrastructure across attempts will lower their costs of doing business. This reuse, however, creates an effective angle for mitigation, as defenders can recognize domain names, attachments, tools, or systems used in a previous compromisation attempt, significantly increasing the cost to the adversary as it would become necessary to recreate the attack infrastructure each time. However, generating such cyber threat intelligence (CTI) is resource-intensive, so organizations often turn to CTI providers that commercially sell feeds with such indicators. As providers have different sources and methods to obtain their data, the coverage and relevance of feeds will vary for each of them. To cover the multitude of threats one organization faces, they are best served by obtaining feeds from multiple providers. However, these feeds may overlap, causing an organization to pay for indicators they already obtained through another provider. This paper presents a privacy-preserving protocol that allows an organization to query the databases of multiple data providers to obtain an estimate of their total coverage without revealing the data they store. In this way, a customer can make a more informed decision on their choice of CTI providers. We implement this protocol in Rust to validate its performance experimentally: When performed between three CTI providers who collectively have 20,000 unique indicators, our protocol takes less than 6 seconds to execute. The code for our experiments is freely available.@en