We, as oral physicians, do an extensive literature search to find out the best diagnostic investigation or best therapeutic option for a disease that we encounter in clinical practice. But do we pause for a moment and look at the literature to see whether the researchers have formulated the most specific focused research question in their works? Failure in this first step to formulate the most appropriate research question would subsequently affect the entire research process. In the arena of research, there are numerous players whose expectations need to be taken into consideration before a good research question is formulated. Foremost among them are our clients – the patients for whom we are doing research. We need to initially ascertain what the patient is expecting from us as a treatment for his ailment and what changes he is expecting in his Quality of Life. The second is our (investigator) views about the intended endpoints for a particular problem we are trying to solve. Third, unfortunately, we often neglect to ascertain the opinions of our co-workers regarding our proposed work. They might have a different perspective on our thought process. Finally, if we have agencies to fund our work, we need to ascertain that they are on board with us to support our research, agreeing with the problem for which we are trying to find a solution. If we analyze from the point of each stake holder mentioned above, each view is logical and it becomes a herculean task for the investigator to formulate and evolve the most appropriate research question. As a guide, researchers could follow the following criteria[1]: - The most appropriate question which is important to patient well-being - The most appropriate question relevant to our knowledge levels - The most appropriate question that can be addressed in a specific time frame - The most appropriate question that would interest you, your team and your patients the most - The most appropriate question that is likely to repeatedly present itself in your practice. As a piece of advice, I would state that, the scientific community always recognizes and appreciates researchers who address problems of diseases faced in their own local community and not some diseases which are very rare in a particular geographic setting. A researcher should always formulate an “Answerable” question. Here, a large broad topic needs to be split into smaller manageable units, which can then be addressed through a standardized protocol.[2] A young researcher by nature would be too ambitious to make a path breaking research to solve all problems. But, seldom does it happen. If your research question is too wide, you end up, lacking rigor in methodology. If your question lacks focus, it is almost close to impossible to replace it with another question once your work is commenced or completed. All questions you want to answer should follow the PICO format. This format is suggested because it helps you to specifically narrow down, refine and formulate your question to address one specific problem. Though PICO format is meant to address interventions, other research questions (diagnostic, prognostic, patient expectations) can be reframed to follow PICO format. The next key element of good scientific research is choosing the appropriate study design. We need to mandatorily sit with trained “BIOSTATISTICIANS”, explain our intended work and zero in on the most appropriate study design. Researchers need to do a thorough data search to find out how a similar question was designed and studied. As you navigate this process you will find your primary research question getting more and more focused which would help you to reframe your PICO components. The above exercise is definitely time consuming and test your patience, but remember – the extra time spent in this stage would save you many hours later if you proceed with an irrelevant research question, inappropriate study design or work on a topic which has already been exhaustively analyzed. Researchers feel quantitative research where you can categorize all parameters with numerical data is superior to qualitative research. But we oral physicians deal with a plethora of disorders especially oro-facial pain where the need to address emotions, feelings are more important than aiming at numerical value changes as a proof of your successful patient management. Qualitative research design are better suited address research questions dealing with feelings and emotions.[3] The NHMRC evidence hierarchy categorizes the most appropriate study design for specific type of research questions – interventional, diagnostic accuracy, prognosis, etiology, and screening.[4] On the contrary, in qualitative research, it is advisable to follow a typical practice-based approach to analyze the data through either a case study, grounded theory, phenomenology, ethnography,ethno methodology and narrative research. To conclude, researchers need to focus on the two essential pillars – the most appropriate answerable focused clinical question and choosing the most appropriate study design to specifically answer the formulated question. However well a question is framed and study is designed, the results will open the door for a next question to be investigated. That is how science grows and progresses!!
The recent MIP*=RE theorem of Ji, Natarajan, Vidick, Wright, and Yuen shows that the complexity class MIP* of multiprover proof systems with entangled provers contains all recursively enumerable languages. Prior work of Grilo, Slofstra, and Yuen [FOCS '19] further shows (via a technique called simulatable codes) that every language in MIP* has a perfect zero knowledge (PZK) MIP* protocol. The MIP*=RE theorem uses two-prover one-round proof systems, and hence such systems are complete for MIP*. However, the construction in Grilo, Slofstra, and Yuen uses six provers, and there is no obvious way to get perfect zero knowledge with two provers via simulatable codes. This leads to a natural question: are there two-prover PZK-MIP* protocols for all of MIP*? In this paper, we show that every language in MIP* has a two-prover one-round PZK-MIP* protocol, answering the question in the affirmative. For the proof, we use a new method based on a key consequence of the MIP*=RE theorem, which is that every MIP* protocol can be turned into a family of boolean constraint system (BCS) nonlocal games. This makes it possible to work with MIP* protocols as boolean constraint systems, and in particular allows us to use a variant of a construction due to Dwork, Feige, Kilian, Naor, and Safra [Crypto '92] which gives a classical MIP protocol for 3SAT with perfect zero knowledge. To show quantum soundness of this classical construction, we develop a toolkit for analyzing quantum soundness of reductions between BCS games, which we expect to be useful more broadly. This toolkit also applies to commuting operator strategies, and our argument shows that every language with a commuting operator BCS protocol has a two prover PZK commuting operator protocol.
This review presents a comprehensive analysis of contemporary scholarship pertaining to instant messaging (IM) user behavior and security protocols. Through meticulous selection, the authors highlight critical studies that illuminate optimized message consumption strategies and delve into the evolving landscape of IM security models. Focusing on the past four years, the review meticulously dissects cutting-edge advancements in this domain. A significant insight emerges: achieving optimal communication security necessitates the synergistic convergence of three fundamental techniques: end-to-end encryption for data confidentiality, decentralized authentication for independent user verification, and zero-knowledge proof for identity obscurity. The review postulates that the simultaneous integration of these elements within the application architecture is paramount for robust privacy and heightened security in the realm of IM.
This research paper explores the intersection of zero-knowledge proofs (ZKPs) and machine learning (ML), presenting a comprehensive overview of recent advancements, applications, and challenges in this fast growing area. The jointers of ZKPs and ML techniques shall go a meter further to fuse privacy, security, and integrity in a number of solutions, which include forming of groups for data sharing and safe machine learning. Through the investigation of the well-respected sites in that area and also the thorough description of formulas and their experimental outcome, this paper looks for the clarification of the current state of affairs and the possible future directions of ZKPs in the AI world. By inserting the verification mechanism of ZKPs into machine learning ecosystem, it allows devising novel solutions for the problems of privacy and confidentiality that have for long been not solved. With this approach, the concatenation of parties collectively performs the process of dealing with private inputs without revealing any of these data and this, in return, opens the possibilities of secure multi-party computation. Furthermore, ZKPs protect data sharing as it gives people the opportunity to construct confidential data and share them to model training without compromising any one’s private details. Being a part of the dynamic conversations, which focus on the game-changing capacity of transparent zero-knowledge proofs (ZKPs), this paper brings the role of ZKPs in preserving the confidentiality and integrity of artificial intelligence (AI) applications into the centre of attention. As scientists still fight to improve protocols and circumvent computational complications, ZKPs are likely to establishment as critical tools in the effort to increase ML systems in the digital sphere.
Open access
Online Learning and Analytics
Intelligent Tutoring Systems and Adaptive Learning
Santiago Martínez, Agustín Ameigenda, Braian De Barros, Guzmán Llambías · 6 authors
Zero-knowledge proofs (zkp) have been used to improve several blockchain limitations (e.g. privacy, scalability), and recent work proposed its usage to improve blockchain interoperability solutions in certain scenarios. However, more studies are needed to understand the full potential of zkp in this context. In particular, zkp may improve existing blockchain interoperability solutions, and help software architects and developers to reduce barriers for blockchain adoption. In this paper, we empirically analyse how zkp may improve a gateway-based interoperability solution. The results showed that it was possible to improve the selected solution and incorporate anonymous cross-chain authentication and private data exchange. A prototype was developed and evaluated using three strategies: 1) its application in a use case scenario, 2) performance tests, and 3) cost analysis. The evaluation showed that the approach is technically feasible, but not suitable for every use case. Furthermore, the private data exchange approach confirmed the results of other studies: zkp is not mature enough for some scenarios, and more work needs to be performed.
Decentralized Identifiers have recently expanded into Internet of Things devices and are crucial in securing users' digital identities and data. However, Decentralized Identifiers face challenges in scenarios necessitating authority delegation and anonymity, such as when dealing with legal guardianship for minors, device loss or damage, and specific medical contexts involving patient information. This paper aims to strengthen data sovereignty within the Decentralized Identifier system by implementing a secure authority delegation and anonymity scheme. It suggests optimizing verifiable presentations by utilizing a sequential aggregate signature, a Non-Interactive Zero-Knowledge Proof, and a Merkle tree to prevent against linkage and Sybil attacks while facilitating delegation. This strategy mitigates security risks related to delegation and anonymity, efficiently reduces the computational and verification efforts for signatures, and reduces the size of verifiable presentations by about 1.2 to 2 times.
In virtual power plants, diverse business scenarios involving user data, such as queries, transactions, and sharing, pose significant privacy risks. Traditional attribute-based encryption (ABE) methods, while supporting fine-grained access, fall short of fully protecting user privacy as they require attribute input, leading to potential data leaks. Addressing these limitations, our research introduces a novel privacy protection scheme using zero-knowledge proof and distributed attribute-based encryption (DABE). This method innovatively employs Merkel trees for aggregating user attributes and constructing commitments for zero-knowledge proof verification, ensuring that user attributes and access policies remain confidential. Our solution not only enhances privacy but also fortifies security against man-in-the-middle and replay attacks, offering attribute indistinguishability and tamper resistance. A comparative performance analysis demonstrates that our approach outperforms existing methods in efficiency, reducing time, cost, and space requirements. These advancements mark a significant step forward in ensuring robust user privacy and data security in virtual power plants.
To address the challenge of balancing privacy protection with regulatory oversight in blockchain transactions, we propose a regulatable privacy protection scheme for blockchain transactions. Our scheme utilizes probabilistic public-key encryption to obscure the true identities of blockchain transaction participants. By integrating commitment schemes and zero-knowledge proof techniques with deep learning graph neural network technology, it provides privacy protection and regulatory analysis of blockchain transaction data. This approach not only prevents the leakage of sensitive transaction information, but also achieves regulatory capabilities at both macro and micro levels, ensuring the verification of the legality of transactions. By adopting an identity-based encryption system, regulatory bodies can conduct personalized supervision of blockchain transactions without storing users’ actual identities and key data, significantly reducing storage computation and key management burdens. Our scheme is independent of any particular consensus mechanism and can be applied to current blockchain technologies. Simulation experiments and complexity analysis demonstrate the practicality of the scheme.
S. B. Goyal, Anand Singh Rajawat, Manoj Kumar, Prerna Agarwal
INTRODUCTION: Cloud computing's offshoot, fog computing, moves crucial data storage, processing, and networking capabilities closer to the people who need them. There are certain advantages, such improved efficiency and lower latency, but there are also some major privacy and security concerns. For these reasons, this article presents a new paradigm for fog computing that makes use of blockchain and Artificial Intelligence (AI). OBJECTIVES: The main goal of this research is to create and assess a thorough framework for fog computing that incorporates AI and blockchain technology. With an emphasis on protecting the privacy and integrity of data transactions and streamlining the management of massive amounts of data, this project seeks to improve the security and privacy of Industrial Internet of Things (IIoT) systems that are cloud-based. METHODS: Social network analysis methods are utilised in this study. The efficiency and accuracy of data processing in fog computing are guaranteed by the application of artificial intelligence, most especially Support Vector Machine (SVM), due to its resilience in classification and regression tasks. The network's security and reliability are enhanced by incorporating blockchain technology, which creates a decentralised system that is tamper resistant. To make users' data more private, zero-knowledge proof techniques are used to confirm ownership of data without actually disclosing it. RESULTS: When applied to fog computing data, the suggested approach achieves a remarkable classification accuracy of 99.8 percent. While the consensus decision-making process of the blockchain guarantees trustworthy and secure operations, the support vector machine (SVM) efficiently handles massive data analyses. Even in delicate situations, the zero-knowledge proof techniques manage to keep data private. When these technologies are integrated into the fog computing ecosystem, the chances of data breaches and illegal access are greatly reduced. CONCLUSION: Fog computing, which combines AI with blockchain, offers a powerful answer to the privacy and security issues with cloud centric IIoT systems. Combining SVM with AI makes data processing more efficient, while blockchain's decentralised and immutable properties make it a strong security measure. Additional security for user privacy is provided via zero-knowledge proofs. Improving the privacy and security of fog computing networks has never been easier than with this novel method.
Zero-knowledge proof (ZKP) systems have surged attention and held a fundamental role in contemporary cryptography. Zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) protocols dominate the ZKP usage, implemented through arithmetic circuit programming paradigm. However, underconstrained or overconstrained circuits may lead to bugs. The former refers to circuits that lack the necessary constraints, resulting in unexpected solutions and causing the verifier to accept a bogus witness, and the latter refers to circuits that are constrained excessively, resulting in lacking necessary solutions and causing the verifier to accept no witness. This paper introduces a novel approach for pinpointing two distinct types of bugs in ZKP circuits. The method involves encoding the arithmetic circuit constraints to polynomial equation systems and solving them over finite fields by the computer algebra system. The classification of verification results is refined, greatly enhancing the expressive power of the system. A tool, AC4, is proposed to represent the implementation of the method. Experiments show that AC4 demonstrates a increase in the solved rate, showing a 29% improvement over Picus and CIVER, and a slight improvement over halo2-analyzer, a checker for halo2 circuits. Within a solvable range, the checking time has also exhibited noticeable improvement, demonstrating a magnitude increase compared to previous efforts.
Mohammed Alghazwi, Dewi Davies-Batista, Dimka Karastoyanova, Fatih Türkmen
Aggregate statistics play an important role in extracting meaningful insights from distributed data while preserving privacy. A growing number of application domains, such as healthcare, utilize these statistics in advancing research and improving patient care. In this work, we explore the challenge of input validation and public verifiability within privacy-preserving aggregation protocols. We address the scenario in which a party receives data from multiple sources and must verify the validity of the input and correctness of the computations over this data to third parties, such as auditors, while ensuring input data privacy. To achieve this, we propose the "VPAS" protocol, which satisfies these requirements. Our protocol utilizes homomorphic encryption for data privacy, and employs Zero-Knowledge Proofs (ZKP) and a blockchain system for input validation and public verifiability. We constructed VPAS by extending existing verifiable encryption schemes into secure protocols that enable N clients to encrypt, aggregate, and subsequently release the final result to a collector in a verifiable manner. We implemented and experimentally evaluated VPAS with regard to encryption costs, proof generation, and verification. The findings indicate that the overhead associated with verifiability in our protocol is 10x lower than that incurred by simply using conventional zkSNARKs. This enhanced efficiency makes it feasible to apply input validation with public verifiability across a wider range of applications or use cases that can tolerate moderate computational overhead associated with proof generation.
Deep learning-based image generation has seen significant advancements with diffusion models, notably improving the quality of generated images. Despite these developments, generating images with unseen characteristics beneficial for downstream tasks has received limited attention. To bridge this gap, we propose Style-Extracting Diffusion Models, featuring two conditioning mechanisms. Specifically, we utilize 1) a style conditioning mechanism which allows to inject style information of previously unseen images during image generation and 2) a content conditioning which can be targeted to a downstream task, e.g., layout for segmentation. We introduce a trainable style encoder to extract style information from images, and an aggregation block that merges style information from multiple style inputs. This architecture enables the generation of images with unseen styles in a zero-shot manner, by leveraging styles from unseen images, resulting in more diverse generations. In this work, we use the image layout as target condition and first show the capability of our method on a natural image dataset as a proof-of-concept. We further demonstrate its versatility in histopathology, where we combine prior knowledge about tissue composition and unannotated data to create diverse synthetic images with known layouts. This allows us to generate additional synthetic data to train a segmentation network in a semi-supervised fashion. We verify the added value of the generated images by showing improved segmentation results and lower performance variability between patients when synthetic images are included during segmentation training. Our code will be made publicly available at [LINK].
The explosive growth of Large-Language Models (LLMs), particularly Generative Pre-trained Transformer (GPT) models, has revolutionised fields ranging from natural language processing to creative writing. Yet, their reliance on vast, often unverified data sources introduces a critical vulnerability: unreliability and security concerns. Traditional GPT models, while impressive in their capabilities, struggle with limited factual accuracy and susceptibility to manipulation by biased or malicious data. This poses a significant risk in professional and personal environments where sensitive or mission-critical data is paramount. This work tackles this challenge head-on by proposing a novel approach to enhance GPT security and reliability: leveraging Zero-Knowledge Proofs (ZKPs). Unlike traditional cryptographic methods that require sensitive data exchange, ZKPs allow one party to convincingly prove the truth of a statement, without revealing the underlying information. In the context of GPTs, ZKPs can validate the legitimacy and quality of data sources used in GPT computations, combating data manipulation and misinformation. This ensures trustworthy outputs, even when incorporating third-party data (TPD). ZKPs can securely verify user identities and access privileges, preventing unauthorised access to sensitive data and functionality. This protects critical information and promotes responsible LLM usage. ZKPs can identify and filter out manipulative prompts designed to elicit harmful or biased responses from GPTs. This safeguards against malicious actors and promotes ethical LLM development. ZKPs facilitate training specialised GPT models on targeted datasets, resulting in deeper understanding and more accurate outputs within specific domains. This allows the creation of ‘expert-GPT’ applications in specialised fields like healthcare, finance, and legal services. The integration of ZKPs into GPT models represents a crucial step towards overcoming trust and security barriers. Our research demonstrates the viability and efficacy of this approach, with our ZKP-based authentication system achieving promising results in data verification, user control, and malicious prompt detection. These findings lay the groundwork for a future where GPTs, empowered by ZKPs, operate with unwavering integrity, fostering trust and accelerating ethical AI development across diverse domains.
In an era marked by technological advancements and a growing demand for secure and transparent electoral processes, the integration of blockchain technology into online voting systems has emerged as a promising solution.This research paper presents a comprehensive exploration of the design, implementation, and implications of an online voting system built upon blockchain technology.Through an in-depth analysis of existing electronic voting challenges and the potential of blockchain, this paper demonstrates how the decentralized, immutable, and transparent nature of blockchain addresses critical concerns such as security, voter privacy, and trust in electoral outcomes.The paper delves into the core architecture of the proposed system, highlighting the role of smart contracts in automating voting processes while ensuring authenticity and verifiability.Security and transparency are examined in detail, showcasing the cryptographic measures that safeguard voter information and prevent fraudulent activities.The challenges of voter authentication, scalability, and accessibility are discussed, along with potential solutions to overcome these obstacles.Drawing on case studies of real-world implementations, the paper offers insights into the successes, challenges, and lessons learned from adopting blockchain-based online voting systems.Legal and ethical considerations are also explored, emphasizing the need for aligning technological innovations with legal frameworks and ethical standards.Finally, the research paper contemplates the future of blockchain-powered online voting, envisioning how emerging technologies such as biometrics, artificial intelligence, and zero-knowledge proofs could further enhance the security and inclusivity of electoral processes.Overall, this paper underscores the transformative potential of blockchain in revolutionizing online voting, fostering a more resilient and democratic electoral landscape.
Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Distinct Identity (zk-PoDI,) which allows a vehicle to prove that it is not the owner of another pseudonym in the local area, without revealing its actual identity. Zk-PoDI is based on the Diophantine equation and zk-SNARK, and does not rely on any specific pseudonym design or infrastructure assistance. We show that zk-PoDI satisfies all the requirements for a practical Sybil-resistance pseudonym system, and it has low latency, adjustable difficulty, moderate computation overhead, and negligible communication cost. We also discuss the future work of implementing and evaluating zk-PoDI in a realistic city-scale simulation environment.
Xi Lin, Heyang Cao, Feng-Hao Liu, Zhedong Wang · 5 authors
Abstract Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are cryptographic protocols that offer efficient and privacy-preserving means of verifying NP language relations and have drawn considerable attention for their appealing applications, e.g., verifiable computation and anonymous payment protocol. Compared with the pre-quantum case, the practicability of this primitive in the post-quantum setting is still unsatisfactory, especially for the space complexity. To tackle this issue, this work seeks to enhance the efficiency and compactness of lattice-based zk-SNARKs, including proof length and common reference string (CRS) length. In this paper, we develop the framework of square span program-based SNARKs and design new zk-SNARKs over cyclotomic rings. Compared with previous works, our construction is without parallel repetition and achieves shorter proof and CRS lengths than previous lattice-based zk-SNARK schemes. Particularly, the proof length of our scheme is around $$23.3\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>23.3</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> smaller than the recent shortest lattice-based zk-SNARKs by Ishai et al. (in: Proceedings of the 2021 ACM SIGSAC conference on computer and communications security, pp 212–234, 2021), and the CRS length is $$3.6\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>3.6</mml:mn> <mml:mo>×</mml:mo> </mml:mrow> </mml:math> smaller. Our constructions follow the framework of Gennaro et al. (in: Proceedings of the 2018 ACM SIGSAC conference on computer and communications security, pp 556–573, 2018), and adapt it to the ring setting by slightly modifying the knowledge assumptions. We develop concretely small constructions by using module-switching and key-switching procedures in a novel way.
We construct perfect zero-knowledge probabilistically checkable proofs (PZK-PCPs) for every language in #P. This is the first construction of a PZK-PCP for any language outside BPP. Furthermore, unlike previous constructions of (statistical) zero-knowledge PCPs, our construction simultaneously achieves non-adaptivity and zero knowledge against arbitrary (adaptive) polynomial-time malicious verifiers. Our construction consists of a novel masked sumcheck PCP, which uses the combinatorial nullstellen- satz to obtain antisymmetric structure within the hypercube and randomness outside of it. To prove zero knowledge, we introduce the notion of locally simulatable encodings: randomised encodings in which every local view of the encoding can be efficiently sampled given a local view of the message. We show that the code arising from the sumcheck protocol (the Reed–Muller code augmented with subcube sums) admits a locally simulatable encoding. This reduces the algebraic problem of simulating our masked sumcheck to a combinatorial property of antisymmetric functions.
The scalability limitations of public blockchains have hindered their widespread adoption in real-world applications. While the Ethereum community is pushing forward in zk-rollup (zero-knowledge rollup) solutions, such as introducing the ``blob transaction'' in EIP-4844, Layer 2 networks encounter a data availability problem: storing transactions completely off-chain poses a risk of data loss, particularly when Layer 2 nodes are untrusted. Additionally, building Layer 2 blocks requires significant computational power, compromising the decentralization aspect of Layer 2 networks. This paper introduces new techniques to address the data availability and decentralization challenges in Layer 2 networks. To ensure data availability, we introduce the concept of ``proof of download'', which ensures that Layer 2 nodes cannot aggregate transactions without downloading historical data. Additionally, we design a ``proof of storage'' scheme that punishes nodes who maliciously delete historical data. For decentralization, we introduce a new role separation for Layer 2, allowing nodes with limited hardware to participate. To further avoid collusion among Layer 2 nodes, we design a ``proof of luck'' scheme, which also provides robust protection against maximal extractable value (MEV) attacks. Experimental results show our techniques not only ensure data availability but also improve overall network efficiency, which implies the practicality and potential of our techniques for real-world implementation.
Priced oblivious transfer (POT) is a cryptographic protocol designed for privacy-preserving e-commerce of digital content. It involves two parties: the merchant, who provides a set of priced items as input, and a customer, who acquires one of them. After the protocol has run, the customer obtains the item they chose, while the merchant cannot determine which one. Moreover, the protocol guarantees that the customer gets the content only if they have paid the price established by the merchant. In a recent paper, the authors proposed a POT system where the payments employed e-coin transactions. The strong point of the proposal was the absence of zero-knowledge proofs required in preceding systems to guarantee the correctness of payments. In this paper, we propose a novel e-coin-based POT system with a fast item retrieval procedure whose running time does not depend on the number of items for sale. This is an improvement over the aforementioned existing proposal whose execution time becomes prohibitively long when the catalog is extensive. The use of zero-knowledge proofs is neither required.
The advent of the digital era and computer-based remote communications has significantly enhanced the applicability of various sciences over the past two decades, notably data science (DS) and cryptography (CG). Data science involves clustering and categorizing unstructured data, while cryptography ensures security and privacy aspects. Despite certain CG laws and requirements mandating fully randomized or pseudonoise outputs from CG primitives and schemes, it appears that CG policies might impede data scientists from working on ciphers or analyzing information systems supporting security and privacy services. However, this study posits that CG does not entirely preclude data scientists from operating in the presence of ciphers, as there are several examples of successful collaborations, including homomorphic encryption schemes, searchable encryption algorithms, secret-sharing protocols, and protocols offering conditional privacy. These instances, along with others, indicate numerous potential solutions for fostering collaboration between DS and CG. Therefore, this study classifies the challenges faced by DS and CG into three distinct groups: challenging problems (which can be conditionally solved and are currently available to use; e.g., using secret sharing protocols, zero-knowledge proofs, partial homomorphic encryption algorithms, etc.), open problems (where proofs to solve exist but remain unsolved and is now considered as open problems; e.g., proposing efficient functional encryption algorithm, fully homomorphic encryption scheme, etc.), and hard problems (infeasible to solve with current knowledge and tools). Ultimately, the paper will address specific solutions and outline future directions to tackle the challenges arising at the intersection of DS and CG, such as providing specific access for DS experts in secret-sharing algorithms, assigning data index dimensions to DS experts in ultra-dimension encryption algorithms, defining some functional keys in functional encryption schemes for DS experts, and giving limited shares of data to them for analytics.
Yutian Chen, Cong Peng, Yu Dai, Min Luo · 5 authors
Multi-scalar multiplication (MSM) is an important building block in most of elliptic-curve-based zero-knowledge proof systems, such as Groth16 and PLONK. Recently, Lu et al. proposed cuZK, a new parallel MSM algorithm on GPUs. In this paper, we revisit this scheme and present a new GPU-based implementation to further improve the performance of MSM algorithm. First, we propose a novel method for mapping scalars into Pippenger’s bucket indices, largely reducing the number of buckets compared to the original Pippenger algorithm. Second, in the case that memory is sufficient, we develop a new efficient algorithm based on homogeneous coordinates in the bucket accumulation phase. Moreover, our accumulation phase is load-balanced, which means the parallel speedup ratio is almost linear growth as the number of device threads increases. Finally, we also propose a parallel layered reduction algorithm for the bucket aggregation phase, whose time complexity remains at the logarithmic level of the number of buckets. The implementation results over the BLS12-381 curve on the V100 graphics card show that our proposed algorithm achieves up to 1.998x, 1.821x and 1.818x speedup compared to cuZK at scales of 221, 222, and 223, respectively.
Blockchain technology has emerged in e-business as a revolutionary force in the realm of cybersecurity, offering novel solutions to the escalating challenges posed by the ever-evolving modern technology landscape. This study delves new e-business into the critical determinants that drive the application of blockchain technology in enhancing cybersecurity. With the proliferation of digital assets and sensitive information, safeguarding data integrity, confidentiality, and accessibility has become paramount. The research employs a multidisciplinary approach in e-business, drawing from the fields of cybersecurity, blockchain technology, and data management. It examines the intricate interplay between blockchain's fundamental features and their direct impact on fortifying cybersecurity measures. Key determinants explored include: Decentralization: Blockchain's decentralized architecture reduces the vulnerability of a single point of failure, rendering it more resilient against cyberattacks. Immutable Ledger: The immutable ledger ensures the integrity of stored data, making it exceptionally challenging for malicious actors to tamper with information. Smart Contracts in E-Business: The automation capabilities of smart contracts enhance the enforcement of security protocols, bolstering the protection of digital assets. Cryptography: Robust cryptographic techniques underpin blockchain security, safeguarding data from unauthorized access. Transparency: The transparent nature of blockchain allows for real-time monitoring and auditing, facilitating the early detection of security breaches. Data Privacy: Blockchain's data privacy features, including permissioned networks and zero-knowledge proofs, address concerns surrounding privacy in cybersecurity. The study also investigates real-world applications and e-business of blockchain technology in cybersecurity across industries, from financial services to healthcare and supply chain management. It highlights successful case studies and identifies the challenges and limitations faced in implementing blockchain solutions. In conclusion, this research underscores the transformative potential of blockchain technology in e-business by fortifying cybersecurity measures. By examining the critical determinants that drive its application, organizations and policymakers can make informed decisions regarding the adoption of blockchain to enhance cybersecurity in the modern technology era. The study encourages further exploration and innovation in this dynamic and rapidly evolving field to secure our digital future.
Abstract To meet the demand for high‐quality healthcare services, data trading can effectively promote the circulation of medical data and improve the level of healthcare services. To address the existing problems of data regulation difficulties and data privacy leakage in medical data trading, a trusted and regulated data trading scheme based on blockchain and zero‐knowledge proof is proposed. In this scheme, a regulatory institution is introduced to control the issuance of authorized tokens and ensure the controllability of data sharing activities. The blockchain takes over the task of generating public parameters to reduce the computational overhead of the system. Based on homomorphic proxy re‐encryption technology, users can perform data analysis in the cloud to ensure data security. Smart contracts and zero‐knowledge proof technology can automatically verify the validity of data to protect the rights and interests of data users; at the same time, efficient consensus algorithms can also increase the rate of transactions processed by the blockchain system. Finally, as the security and performance analysis shows, the scheme in this paper has better security, higher efficiency and more comprehensive functions.