Recommendation systems are extensively used to provide personalised services. As a commonly used recommendation algorithm, collaborative filtering (CF) provides predictions of the target user's ratings from the ratings of other users. However, collecting other users' personalised information violates the user's privacy. Although encryption-based privacy-preserving collaborative filtering protocols have been proposed, the existing solutions still suffer from security issues that arise during the recommendation processes and inefficiencies that originated from the techniques used. Motivated by the deficiencies of the existing protocols, this study aims to investigate an efficient and robust privacy-preserving collaborative filtering protocol. It strives to enhance the performance of recommendation systems while ensuring user privacy. Firstly, the proposed protocol ensures privacy for all participants. Secondly, the proposed protocol achieves better computation efficiency. In contrast to the previous work that used packings and homomorphic rotations to compute the similarities, the proposed protocol uses coefficient embedding and re-encryption. The similarity computation process achieves about 8 & times; more efficiency than the state-of-the-art. However, the space cost of our protocol is somewhat larger. Last but not least, the proposed protocol maintains full rating information, while the previous works only maintain the rounding information of the target user. Theoretically, the proposed protocol provides better accuracy. In addition, the proposed protocol achieves post-quantum security. The security of the proposed protocol is analyzed qualitatively, and quantitative analyses of the computation and communication costs are carried out. The experimental results support the theoretical analysis.

With the popularity of the mobile Internet and smartphones, remote facial recognition is increasingly becoming a new way of verifying identity. Due to privacy concerns in the face recognition protocols, cryptographic tools (e.g., FHE schemes, garbled circuits) are used to protect the facial information. However, the existing protocols use homomorphic encryption schemes in a black-box way, which strongly affects the protocol's performance. Furthermore, the garbled circuit-based face recognition protocols need multiple interactions between the participants, which results in more inefficient protocols. Motivated by the above problems, we propose a more efficient privacy-preserving face verification protocol by tailoring multiple newly designed techniques, which may be of independent interest and may find further applications in related biometric verification protocols. In particular, we introduce a new packing technique that can homomorphically compute multiple inner products simultaneously. Compared with the previous Chinese Remainder Theorem (CRT)-based methods, our packing method works on all the modulus of the ring $\mathbb {Z}_{q}[x]/(x^{n}+1)$. Furthermore, we design a cryptographic hash-based privacy-preserving non-interactive threshold comparison scheme with significantly better communication latency than the garbled circuit-based approach. Applying the above two independent, valuable techniques, our privacy-preserving face verification protocol achieves better space and time efficiency than the previous protocols. Our theoretical analyses are supported by the experimental evaluations.

The proliferation of smart devices has advanced the application of face identification. Since face images involve user privacy, privacy-preserving face identification has attracted significant attention. The main goals of privacy-preserving face identification are 1) achieving privacy against the participants and tasks and 2) achieving better identification performance and running efficiency. The existing homomorphic encryption-based protocols either suffer from large communication overheads or need many homomorphic rotations or homomorphic multiplication during the identification process. Motivated by the deficiencies of these existing approaches, we propose an efficient privacy-preserving face identification protocol that not only has lower communication costs but also achieves better efficiency. To this, a more compact and efficient algorithm for homomorphic computation of Euclidean distances (EDs) is proposed. This homomorphic computation algorithm can homomorphicaly compute two EDs by only proceeding one homomorphic multiplication. Compared with the previous techniques, the proposed method saves about half of the space cost comparing with the single vector embedding methods and is at least $2 \times $ faster than the previous works. In addition, the method is independent of the proposed face identification protocol and thus can improve other biometric identification protocols straightforwardly. The experimental evaluations demonstrate that the proposed protocol not only performs almost the same identification rate as a plain protocol that provides no privacy but also is very efficient in adapting to the resource-limited small smart devices.

Permissioned blockchains play a significant role in various application scenarios. Applications built on heterogeneous permissioned blockchains need to migrate data from one chain to another, aiming to keep their competitiveness and security. Thus, data migration across heterogeneous chains is a building block of permissioned blockchains. However, existing data migration protocols across heterogeneous chains are rarely used in practice since data migration technologies are insecure. To this end, we propose a data migration protocol across permissioned blockchains, named DataFly. We design a peg consensus mechanism, which provides consistent data-migration functionality between any two permissioned blockchains. To preserve the confidentiality of data, we invoke two classical cryptographic methods, i.e., i) ECDSA feature and ii) the integrated signature and public key encryption scheme. Through combining those two methods, data can be securely migrated from one permissioned blockchain to another without exposing the migrated data to anyone except associated parties. To demonstrate the practicality of DataFly, we implement a prototype of DataFly using existing popular permissioned blockchains, i.e., Hyperledger Fabric and private enterprise Ethereum. Measurement results demonstrate that DataFly outperforms related works in terms of transaction latency and gas costs.

The proliferation of smart devices has propelled the advancement of IoT-based spatial crowdsourcing. The issue of location privacy in task allocation for IoT-based spatial crowdsourcing has attracted significant attention. Therefore, the main goals of privacy-preserving spatial crowdsourcing (PriSC) are: 1) achieving better location privacy for both participants and tasks and 2) achieving better allocation performance, i.e., accuracy and average moving distance. The homomorphic encryption-based approaches can achieve these goals, yet they suffer from heavy computation and large communication overhead. Although the differential privacy (DP)-based approaches are very efficient, these approaches leverage allocation performance to achieve better location privacy. Motivated by the deficiencies of these existing approaches, we propose a lightweight hash-based spatial crowdsourcing protocol, which not only protects both task location and participant location from the server but also reduces service providers' computation and communication overhead. Besides, our design is independent of the concrete hash function and thus can be instantiated by any collision-resistant cryptographic hash function. Experiment results demonstrate that our protocol outperforms related works in terms of accuracy and average moving distance.

Sidechain techniques enhance blockchain scalability and interoperability, enabling decentralized exchanges and cross-chain operations for wrapped digital assets. However, existing PoW sidechains face challenges, including centralization, high communication costs, and incomplete PoW-based security proofs. This paper introduces SFPoW, a Secure and Flexible Proof-of-Work sidechains for cross-chain interoperability with wrapped assets. SFPoW facilitates decentralized asset transfers and token swaps across nearly all PoW-based cryptocurrencies without requiring soft or hard forks or fixed PoW targets. It establishes a decentralized, fair validation set within the sidechain, improving adaptability and reducing competition and confirmation periods. A pluggable cross-chain proof generation method is proposed, effectively filtering lazy nodes, incentivizing active participation, and minimizing on-chain verification overhead to a proof size of 200.5 bytes. Through mining behavior analysis and cryptographic reductions, SFPoW satisfies weak and strong atomicity. Experiments on the Ronin blockchain and an Ethereum testnet demonstrate a round-trip cost of $6.55 and latency of 372.1-382.6 seconds, confirming its practicality and efficiency.

Blockchain technology, introduced in 2008, has revolutionized data storage and transfer across sectors such as finance, healthcare, intelligent transportation, and the metaverse. However, the proliferation of blockchain systems has led to discrepancies in architectures, consensus mechanisms, and data standards, creating data and value silos that hinder the development of an integrated multi chain ecosystem. Blockchain interoperability (a.k.a cross chain interoperability) has thus emerged as a solution to enable seamless data and asset exchange across disparate blockchains. In this survey, we systematically analyze over 150 high impact sources from academic journals, digital libraries, and grey literature to provide an in depth examination of blockchain interoperability. By exploring the existing methods, technologies, and architectures, we offer a classification of interoperability approaches including Atomic Swaps, Sidechains, Light Clients, and so on, which represent the most comprehensive overview to date. Furthermore, we investigate the convergence of academic research with industry practices, underscoring the importance of collaborative efforts in advancing blockchain innovation. Finally, we identify key strategic insights, challenges, and future research trajectories in this field. Our findings aim to support researchers, policymakers, and industry leaders in understanding and harnessing the transformative potential of blockchain interoperability to address current challenges and drive forward a cohesive multi-chain ecosystem.

This paper presents an adaptively secure identity-based encryption (IBE) scheme from Learning With Errors (LWE) in the standard model. Compared to the previous LWE-based most compact construction of Yamada (CRYPTO17), one of the distinguishing properties of our IBE scheme is that the master public key size of our IBE scheme is significantly smaller, and our design is explicitly given, and thus all the IBE parameters can be instantiated. To achieve this, we design a more compact homomorphic equality test algorithm over LWE problems, which is significantly better than the previous bit-wise comparison of Yamada (CRYPTO17) and Katsumata (ASIACRYPT17). We show that our homomorphic equality test algorithms can pack a super-constant number of GSW-type bit encodings and thus may find other improvements in other LWE-based crypto schemes.

With the popularity of the mobile Internet, data is increasingly becoming a new resource. Therefore, the trading of such data resources has become an increasing demand. In this paper, we propose a fair privacy-preserving data trading protocol based on blockchain. Firstly, our data trading protocol achieves fairness by carefully combining the probabilistic approaches and the fully homomorphic encryption techniques. Moreover, our protocol allows online arbitration when misbehavior occurs in the trading process is detected. Note that previous data trading protocols need a Trusted Third Party (TTP) or an offline arbitrator to solve disputes, weakening the trust of those protocols. Secondly, the data validity verification process of our protocol is more flexible. Most Importantly, different from all previous designs which only achieve privacy against communication channel eavesdroppers, our protocol achieves privacy against any eavesdropper and the passive arbitrator. The above-distinguishing properties of our protocol are mainly benefited from the homomorphic encryption and double encryption techniques. In addition, our data trading protocol can be instantiated with post-quantum primitives and thus achieves post-quantum security. To demonstrate the feasibility of the proposed protocol, we conduct a comprehensive evaluation with the instantiated cryptographic primitives based on the Ethereum test network.

In this paper, we present quantum algorithms for finding impossible differentials and zero-correlation linear hulls, which are distinguishers for the two powerful attacks against symmetric ciphers of impossible differential attack and zero-correlation linear attack. Compared to classical methods, the proposed quantum algorithms possess many advantages. Firstly, our quantum algorithm for finding impossible differentials obtains the input and output differences by solving linear equation systems instead of searching in a limited space; Secondly, our quantum algorithm for zero-correlation linear hulls can investigate the key schedule’s effect; Thirdly, the only computation cost of our algorithms is solving linear equation systems, and the size of the systems is not increasing as the round number increases. The core idea of our method is to use the Berstein-Vazirani algorithm to find 1-linear structures of Boolean functions. We check the validity of the proposed quantum algorithm with the SIMON block cipher family and RC5 block cipher. We show that the proposed algorithms can discover some 11-round, 12-round, 13-round, 16-round, and 19-round impossible differentials and zero-correlation linear hulls of SIMON cipher when considering the key schedules and 2.5-round impossible differential of RC5 when considering the round subkeys.

In this paper, we propose a lattice-based encryption scheme with a short ciphertext size. Our scheme is somewhat hybrid of the NTRU type encryptions and RLWE based encryptions. In particular, the ciphertext of the scheme is a ring element as NTRU type encryptions, yet it can be compressible as RLWE based encryption schemes. Furthermore, we present a key-encapsulation mechanism that is more efficient than a direct construction from our encryption scheme. The IND-CPA security of the schemes is based on the RLWE assumption and the NTRU assumption. Our parameterizations show that the schemes enjoy almost the same public key size as the NIST PQC finalist lattice-based candidates, yet the ciphertext size is only about 37% of them.

This work constructs an identity based encryption from the ring learning with errors assumption (RLWE), with shorter master public keys and tighter security analysis. To achieve this, we develop three new methods: (1) a new homomorphic equality test method using nice algebraic structures of the rings, (2) a new family of hash functions with natural homomorphic evaluation algorithms, and (3) a new insight for tighter reduction analyses. These methods can be used to improve other important cryptographic tasks, and thus are of general interests.Particularly, our homomorphic equality test method can derive a new method for packing/unpacking GSW-style encodings, showing a new non-trivial advantage of RLWE over the plain LWE. Moreover, our new insight for tighter analyses can improve the analyses of all the currently known partition-based IBE designs, achieving the best of the both from prior analytical frameworks of Waters (Eurocrypt ’05) and Bellare and Ristenpart (Eurocrypt ’09).

In this paper, we propose a public-key encryption scheme based on the Ring Learning With Rounding (RLWR) problem. Our scheme is seen as RLWR based variant of Saber (NIST PQC standardization round 3 candidate scheme). The design motivation is to overcome the very involved security proofs of LWR based public-key encryption schemes. To simplify the previous very involved security proofs, we introduce an intermediate problem which is at least as hard as RLWE problem. In contradiction to the previous LWR based schemes, our construction shares simple and intuitive security proof. We first present an IND-CPA public-key encryption scheme, and then apply a variant of the Fujisaki–Okamoto transforms to create a CCA- secure KEM. Our parameterization of the final KEM and the reference implementation shows that the performance of our scheme is comparable with the NIST PQC standardization round 3 candidates.

Group key exchange schemes allow group members to agree on a session key. Although there are many works on constructing group key exchange schemes, but most of them are based on algebraic problems which can be solved by quantum algorithms in polynomial time. Even if several works considered lattice based group key exchange schemes, believed to be post-quantum secure, but only in the random oracle model. In this work, we propose a group key exchange scheme based on ring learning with errors problem. On contrast to existing schemes, our scheme is proved to be secure in the standard model. To achieve this, we define and instantiate multi-party key reconciliation mechanism. Furthermore, using known compiler with lattice based signature schemes, we can achieve authenticated group key exchange with postquantum security.

One of the Bitcoin’s innovations is the Proof of Work puzzle (aka scratch-off puzzle) as a consensus protocol for anonymous networks without pre-established PKI. Bitcoins based on the Proof of Work puzzle have been harshly blamed today for problems such as energy wasted and not easily scalable. In this paper, we construct a novel Proof of Transaction(PoT) puzzle, and prove that PoT puzzle satisfies the basic construction conditions of scratch-off puzzle. We also show construction of PoTcoin as application. PoTcoin has many advantage but not limited as strengthening the network topology, promoting currency circulation, anti-outsourcing computing and environment-friendly.