Usama Arshad, Zahid Halim, Hisham Alasmary, Muhammad Waqas
Blockchain technology is used often as a merger with other technologies to achieve a high level of security, privacy, and robustness and to handle issues such as maliciousness of nodes, privacy leakage, the selfishness of nodes, communication delays, and high execution and transaction costs. There is currently a lack of a comprehensive system for automating and cost-effectively managing vehicle repairs, maintenance, and other associated services. To solve such issues we proposed a novel futuristic comprehensive model that integrates a blockchain-based framework to safely record vehicle maintenance, validate repair services, and oversee parts inventory. It employs smart contracts and consensus protocols to secure communications and data storage, thus reducing data breaches and vulnerabilities from single-point failures. A reward system is embedded within the network to encourage positive behavior and deter detrimental actions. We also incorporated advanced privacy-ensuring methods, like zero-knowledge proofs and secure multi-party computation, to safeguard sensitive data while preserving its utility. Our model features automatic detection and response mechanisms for node failure, improving network resilience by 25% thus also providing a 20% reduction in execution, operational costs, and scalability with an enhancement of 15%, underscoring the model’s efficiency in vehicular repair and maintenance activities. Results and simulations clearly depict the overall performance and efficiency in terms of security, privacy, node failure, and the management of vehicle repairs with respect to other closely related models.
In a multiparty signing protocol, also known as a threshold signature scheme, the private signing key is shared amongst a set of parties and only a quorum of those parties can generate a signature. Research on multiparty signing has been growing in popularity recently due to its application to cryptocurrencies. Most work has focused on reducing the number of rounds to two, and as a result: (a) are not fully simulatable in the sense of MPC real/ideal security definitions, and/or (b) are not secure under concurrent composition, and/or (c) utilize non-standard assumptions of different types in their proofs of security. In this paper, we describe a simple three-round multiparty protocol for Schnorr signatures that is secure for any number of corrupted parties; i.e., in the setting of a dishonest majority. The protocol is fully simulatable, secure under concurrent composition, and proven secure in the standard model or random-oracle model (depending on the instantiations of the commitment and zero-knowledge primitives). The protocol realizes an ideal Schnorr signing functionality with perfect security in the ideal commitment and zero-knowledge hybrid model (and thus the only assumptions needed are for realizing these functionalities). In our presentation, we do not assume that all parties begin with the message to be signed, the identities of the participating parties and a unique common session identifier, since this is often not the case in practice. Rather, the parties achieve consensus on these parameters as the protocol progresses.
Biscuit is a recent multivariate signature scheme based on the MPC-in-the-Head paradigm. It has been submitted to the NIST competition for additional signature schemes. Signatures are derived from a zero-knowledge proof of knowledge of the solution of a structured polynomial system. This extra structure enables efficient proofs and compact signatures. This short note demonstrates that it also makes these polynomial systems easier to solve than random ones. As a consequence, the original parameters of Biscuit failed to meet the required security levels and had to be upgraded.
Verifiable encryption (VE) is a protocol where one can provide assurance that an encrypted plaintext satisfies certain properties, or relations. It is an important building block in cryptography with many useful applications, such as key escrow, group signatures, optimistic fair exchange, and others. However, the majority of previous VE schemes are restricted to instantiation with specific public-key encryption schemes or relations. In this work, we propose a novel framework that realizes VE protocols using zero-knowledge proof systems based on the MPC-in-the-head paradigm (Ishai et al. STOC 2007). Our generic compiler can turn a large class of zero-knowledge proofs into secure VE protocols for any secure public-key encryption scheme with the undeniability property, a notion that essentially guarantees binding of encryption when used as a commitment scheme. Our framework is versatile: because the circuit proven by the MPC-in-the-head prover is decoupled from a complex encryption function, the work of the prover is focused on proving the encrypted data satisfies the relation, not the proof of plaintext knowledge. Hence, our approach allows for instantiation with various combinations of properties about the encrypted data and encryption functions. We then consider concrete applications, to demonstrate the efficiency of our framework, by first giving a new approach and implementation to verifiably encrypt discrete logarithms in any prime order group more efficiently than was previously known. Then we give the first practical verifiable encryption scheme for AES keys with post-quantum security, along with an implementation and benchmarks.
Teng Cheng, Qiang Liu, Qin Shi, Ze Yang · 7 authors
Near-field communication in VANETs can effectively reduce communication overhead compared to peer-to-peer communication. However, there is still plenty of room for improvements to be made to ensure identity authentication privacy protection and to enhance the security and efficiency of key distributions during transmissions. Therefore, this paper proposes an anonymous identity authentication and group key distribution scheme based on quantum random numbers. In the proposed scheme, (1) anonymous credentials for vehicles are generated by a combination of random numbers on the vehicle side and random numbers in the TA, and mutual recognition of vehicles and roadside identity is achieved through the TA in the form of zero-knowledge proof, which achieves privacy protection for the vehicle during authentication. (2) A combined key generation method was devised. The roadside and the TA in this case jointly generate the group key. The TA uses a previously filled quantum key to encrypt the group session key parameter GSPc generated by its quantum random number generator to ensure security, and the roadside obtains the group session key parameter GSPr by calculating the anonymous credentials of all legitimate vehicles to achieve fast updates of the group session key. This scheme achieves forward and backward security while guaranteeing one-at-a-time encryption. The signaling and computation overheads were calculated, and the signaling overhead was reduced by nearly half. In addition, the group key issuance time was significantly reduced compared with other schemes. Through formal security analysis and experimental verification, the security and feasibility of this protocol were proved.
Blockchain and zero-knowledge (ZK) proof techniques have advanced greatly in recent years, largely spurred by cryptocurrency development. They enable decentralized coordination of, and proofs of computational integrity in, the execution of privacy-preserving protocols.
The promise of combining blockchain with artificial intelligence (AI) is compelling: auditable data provenance for training sets, tamper-evident logging for model lifecycle events, decentralized marketplaces for models and datasets, and automated enforcement of usage policies via smart contracts. Yet organizations quickly discover that operationalizing blockchain-based AI goes beyond stitching together two popular technologies. Differences in trust assumptions, latency and throughput profiles, security primitives, compliance expectations, and tooling maturity frequently collide at deployment time. This manuscript organizes those frictions into a coherent integration problem space and proposes a reference architecture and evaluation methodology to reason about trade-offs. We review the literature on blockchain consensus and scalability, privacy-preserving machine learning (federated learning, differential privacy, secure computation, and zero-knowledge proofs), data governance and compliance (e.g., GDPR), and MLOps platforms. We then present a methodology that stress-tests seven integration dimensions: architecture and partitioning (on-chain vs. off-chain responsibilities), performance and cost (latency, throughput, gas), privacy and confidentiality (leakage risks and mitigations), security and integrity (tamper-evidence, oracle trust), interoperability (heterogeneous chains and toolchains), compliance and governance (auditability versus erasure rights), and human/organizational fit (DevOps, incident response, and skills).
An all-encompassing and flexible framework that links theoretical principles and actual execution is required for the creation of hardware for Web3.0 and edge intelligence. Decentralized identification, semantic data, blockchain, and zero-knowledge proofs are just few of the concepts that are examined in depth to provide the groundwork for this technique. The main goal is to figure out what kinds of hardware are needed to implement these theoretical principles. Subsequently, the technique continues to extract the hardware requirements, distinguishing the unique demands, problems, and performance criteria important for enabling Web3.0 and edge intelligence. These needs include a wide range of topics, including performance, safety, efficiency, scalability, and even compatibility. Once the theoretical principles and hardware requirements are fully understood, the technique moves on to the design step. Key to realizing these abstract ideas is the development of specialized hardware architectures and components. Based on the outcomes of the performance assessment, iterative refinement is carried out to fix the hardware's flaws and enhance its functionality. As a result of this iterative process, hardware is kept up to date to suit the ever-shifting requirements of Web3.0 and edge intelligence. The suggested technique concludes with an emphasis on flexibility and future-proofing in light of the everchanging nature of Web3.0 and edge intelligence. Keeping up with technology developments and reevaluating the hardware design as needed are both part of this process.
Suppakit Waiwitlikhit, Ion Stoica, Yi Sun, Tatsunori Hashimoto · 5 authors
There is an increasing conflict between business incentives to hide models and data as trade secrets, and the societal need for algorithmic transparency. For example, a rightsholder wishing to know whether their copyrighted works have been used during training must convince the model provider to allow a third party to audit the model and data. Finding a mutually agreeable third party is difficult, and the associated costs often make this approach impractical. In this work, we show that it is possible to simultaneously allow model providers to keep their model weights (but not architecture) and data secret while allowing other parties to trustlessly audit model and data properties. We do this by designing a protocol called ZkAudit in which model providers publish cryptographic commitments of datasets and model weights, alongside a zero-knowledge proof (ZKP) certifying that published commitments are derived from training the model. Model providers can then respond to audit requests by privately computing any function F of the dataset (or model) and releasing the output of F alongside another ZKP certifying the correct execution of F. To enable ZkAudit, we develop new methods of computing ZKPs for SGD on modern neural nets for simple recommender systems and image classification models capable of high accuracies on ImageNet. Empirically, we show it is possible to provide trustless audits of DNNs, including copyright, censorship, and counterfactual audits with little to no loss in accuracy.
To fundamentally solve the "prisoner's dilemma" of the incentive system, we propose an incentive model built from the existing blockchain ecology.It uses blockchain as the underlying technology, federal learning as the operational basis, a decentralized autonomous organization as the organizational form, smart contract as the means of implementation, and non-homogeneous pass-through as the incentive mechanism, and the core method is to use zero-knowledge proof in privacy computing to build a trustworthy and reliable management decision to achieve "power and responsibility matching" more efficiently.
In this chapter, we embark on a journey through the dynamic intersection of blockchain technology and federated machine learning (FML). This chapter elucidates the pivotal role of FML in mitigating data privacy concerns in the ever-expanding field of Artificial Intelligence. This chapter begins by unveiling the intrinsic challenges stemming from centralized data collection and traditional machine learning (ML) methods. It emphasizes the urgent need for innovative solutions that not only enhance learning efficiency but also ensure the confidentiality and security of sensitive data. FML emerges as a promising paradigm where disparate parties collaboratively train ML models without the necessity of centralized data aggregation. This chapter underscores the merits of FML, which encompass data privacy preservation and efficient model training. However, it doesn&s;t shy away from exposing the stumbling blocks encountered, including centralization issues, potential adversarial updates, and the overarching data privacy dilemma. To address these challenges, this chapter introduces blockchain technology as a robust foundation. Ethereum and smart contracts (SCs) take center stage as they empower a decentralized framework for federated learning. An SC assumes the role of a coordinator, ensuring the secure aggregation of model updates. Furthermore, the innovative application of zero-knowledge STARK proofs is employed to verify the legitimacy of client training processes, thus upholding the integrity of data. This chapter concludes by evaluating the performance of this blockchain-facilitated federated learning model, showcasing promising results that are in close proximity to theoretical limits. However, it also highlights trade-offs, including increased time requirements and gas costs associated with blockchain integration. In sum, this chapter sets the stage for an exciting exploration of how blockchain technology can empower federated learning, transforming it into a robust, secure, and efficient approach to ML while safeguarding data privacy. It opens the door to further research and innovation in this rapidly evolving and highly relevant field.
The rapid proliferation of AI-generated “deepfake” images, audio, and video is eroding public trust in digital media and amplifying risks to elections, markets, journalism, and personal safety. While AI detection models have improved, they face an adversarial “cat-and-mouse” problem and often struggle to generalize across manipulation methods and compression regimes. This manuscript proposes and analyzes a hybrid, end-to-end approach that couples upstream provenance and authenticity signals—anchored via open standards (e.g., C2PA Content Credentials) and decentralized ledgers—with downstream AI detection and moderation. The pipeline captures and signs media at source; binds verifiable, tamper-evident metadata; anchors cryptographic hashes on a public or consortium blockchain; stores originals off-chain with content addressing (e.g., IPFS/Filecoin); and fuses these trust signals with model-based detectors and policy engines at distribution edges. We situate the proposal within current regulation (e.g., EU AI Act transparency duties) and state-of-the-art methods (e.g., watermarking such as SynthID, Stable Signature, and Tree-Ring; deepfake detectors trained on DFDC and FaceForensics++), highlighting both strengths and known attack vectors against watermarking that motivate layered defenses. A simulation-based evaluation illustrates that combining provenance signals with video-level transformer detectors can raise F1 from 0.85 to 0.92 while cutting false positives by ~41% in a balanced test set, primarily by rejecting credential-mismatched or hash-divergent media before expensive model inference. We further discuss privacy-preserving verification using W3C Verifiable Credentials (VC 2.0), Decentralized Identifiers (DIDs), and selective-disclosure with zero-knowledge proofs. The findings make a practical case for “trust by design” built on open standards, decentralized integrity proofs, and robust AI detection, implemented as a policy-aware defense-in-depth stack for platforms and newsrooms.
Kimberly DiMaria; C.S. Mott Children’s Hospital, Ann Arbor, MichiganSurvival after in-hospital cardiac arrest is associated with resuscitation team performance and adherence to American Heart Association (AHA) resuscitation algorithms. The primary aim was increased resuscitation team performance during simulation scenarios as evidenced by improved modified Clinical Performance Tool (mCPT) scores. The secondary aim was improved adherence to AHA resuscitation guidelines during actual code events. Process measures, including frequency of simulations, were used to determine proof of concept.In 2021, a review of resuscitation events in the cardiac progressive care unit (CPCU) revealed poor compliance with AHA guidelines. One of the most critical times of code performance in the CPCU is during the initial response, before code team arrival, which prompted development of a simulation intervention that focused on improving team performance during the initial stages of resuscitation. A single-center, prospective, interventional quality improvement project, First 5-Minute Drills, was developed to provide concise, repeated opportunities for CPCU team members to practice low-frequency, high-risk skills, and crisis resource management principles. Team performance during simulations was directly observed and evaluated using an mCPT score, a 20-point scoring tool that measures team performance of 10 different skills. A t test was used to compare mean mCPT scores at 3 periods. Adherence to AHA algorithms during patient codes was assessed via a retrospective review. Patient resuscitations were evaluated using a standardized scorecard comprising 10 evidence-based treatment recommendations embedded within the AHA resuscitation algorithms.Over 19 months, 48 First 5-Minute Drills were conducted and 238 team members participated. The mCPT was administered at various time points during the study, and results demonstrated a 35% improvement in code team performance. There was a statistically significant increase in mCPT scores from 14.3 to 18.3 (P = .02). Pre-post analysis of 4 actual code events demonstrated a 50% improvement in adherence to AHA algorithms. First 5-Minute Drills resulted in improved simulation resuscitation team performance and adherence to AHA algorithms during real-time code events in the CPCU. The low-cost, high-yield First 5-Minute Drills intervention is generalizable to other departments and hospitals.Eunice Santos, Jimmy Nguyen; Cedars-Sinai Medical Center, Los Angeles, CaliforniaThe rate of central line [catheter]–associated bloodstream infections (CLABSIs) with pulmonary arterial catheter (PAC) use is high. In the advanced heart failure unit (AHFU), PAC CLABSIs are associated with increase morbidity, cost, and loss of eligibility for a heart transplant. The goal for this initiative was to determine whether the optimization of a nursing maintenance bundle created in 2018 in the AHFU to reduce PAC CLABSIs sustained fewer than 3 PAC CLABSIs per year for 5 years.In the AHFU, PACs are used for heart transplant evaluation and heart transplant listing. In 2017, there were 10 PAC infections at our institution. Feedback from case reviews for PAC CLABSIs in 2017 showed problems maintaining occlusive dressings in these catheters. In 2018, a quality improvement project was implemented to address challenges associated with PAC maintenance. The project introduced new PAC dressing kits and development of education for nurses caring for PACs in the AHFU. The standard central catheter dressing kit included a single transparent bandage, a chlorhexidine gluconate (CHG) swab, sterile gloves, and tweezers. The new PAC kit included a second anchoring transparent bandage and CHG swab. All the nurses were educated on the use of the PAC dressing kit—specifically the application of 2 transparent bandages to maintain an occlusive dressing, narrow beard clipping, and CHG bathing. In 2021, introduction of weekly central catheter rounds consisted of epidemiology nurse and nursing leadership rounding on patients with PACs. Sustainment of the project included permanent changes in nursing education, dressing kits, and close monitoring by epidemiology nurses and nursing leadership.PAC CLABSI rates were analyzed using the organization’s hospital-acquired infection dashboard from January 2018 to July 2023. Results showed that the PAC nursing maintenance bundle reduced PAC CLABSIs through the 5-year study period. In 2018, the year of implementation, there were 3 PAC CLABSIs in the AHFU. There was 1 PAC CLABSI in the AHFU in each of 2019, 2020, and 2023; there were 2 CLABSIs in 2021. Implementation of the PAC nursing maintenance bundle, multidisciplinary collaborations, continued nursing education, and adherence to the PAC nursing maintenance bundle during the 5-year period accomplished organizational goals in improving CLABSI rates and improving patient outcomes.Isabel Madrigal, Melissa Parodi; Doctors Hospital, Coral Gables, FloridaCentral line [catheter]–associated bloodstream infections (CLABSIs) are among the most common hospital-acquired infections (HAIs). CLABSIs are serious infections that can result in longer hospital length of stay, increased cost, and increased risk for death. In February 2021, the critical care unit (CCU) leadership team reviewed the unit CLABSI rates of 11.33 for the third quarter (Q3) of calendar year 2020 (CY20) and 9.32 for Q4 CY20. 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D Davidson, Nicola Vasey, Adam Pattison Rathbone, Charlotte Lucy Richardson
Abstract Introduction Pharmacy education in the United Kingdom must adapt to produce independent prescribing pharmacists ready to join an evolving healthcare system. Current placement practices exclude approximately 21% of the population due to a lack of specific recommendations surrounding paediatric experience and knowledge within the MPharm programme.[1] In turn, students’ current experiences may be limited by lack of interaction with unique learning outcomes offered by some healthcare settings, such as paediatric hospitals.[2] To promote student exposure in an overlooked speciality, novel approaches to work-based learning can be utilised. Aim This study aimed to explore pharmacy students’ experiences of work-based learning in a paediatric hospital setting. Methods In October 2022, fourth-year MPharm students at one school of pharmacy were invited to undertake work-based learning sessions across one academic year. The sessions aimed to develop students’ paediatric consultations skills and knowledge. Sessions consisted of a briefing, ward activities, scaffolded consultations with children and carers, and debriefs with a clinical supervisor. Debriefs included students reporting clinical information, required action and learning outcomes. All debriefs provided by students were transcribed by a clinical supervisor using a spreadsheet which recorded the date, ward visited, patient details, student handover, follow-up (if required) and learning outcomes. Data was initially cleaned, quality checked, and underwent content analysis to identify patterns and key themes to describe student experiences. Results Seventy-four students took part in sessions and delivered 233 consultations covering the medical history of the patient (76%, n=177), with varied levels of completeness. Students were exposed to acute conditions (41%, n=96) and chronic conditions (33%, n=76), with 13% (n=30) still awaiting diagnosis. Forty-eight percent (n=81) of learning points related to the pathology, diagnosis and symptoms of conditions, 24% (n=41) to medicines, 15% (n=25) to patient care, 11% (n=18) to non-clinical experiences and 2% (n=4) to other outcomes. In addition to carrying out ward activities, students underwent the processing of experiences during post-session debriefs: “It’s uncomfortable seeing a child struggle to breathe” [P131]. The process of active reflection was also evidenced in debriefs: “I felt very anxious, like a tightness in the chest, to hear that a child had a short life expectancy” [P145]; “I realised they had zero cultural competence after seeing a patient from the Middle East with jaundice” [P233]. Conclusion The study demonstrates a proof of concept that students can be exposed to complex care needs and challenging consultations under indirect supervision, demonstrating the paediatric setting to be a suitable work-based learning host. However, findings are limited to a single cohort of students at a single site, meaning transferability may be limited. Future studies could focus on longitudinal educational and emotional outcomes of students by measuring clinical knowledge, competence and confidence. Utilising post-session debriefs with peers and supervisors created a space to share both pharmaceutical and emotional learning points, aiding in managing the cognitive load of students. This experience not only highlights the requirement of paediatric exposure in pharmacy education programmes to aid the students’ future practice, but the importance of supervised reflective activities following work-based learning experiences. References 1. Office for National Statistics. Ethnic group by age and sex, England and Wales: Census 2021. 2023. Available from: https://www.ons.gov.uk/peoplepopulationandcommunity/culturalidentity/ethnicity/articles/ethnicgroupbyageandsexenglandandwales/census2021 2. Kerth J-L, van Treel L, Bosse HM. The Use of Entrustable Professional Activities in Pediatric Postgraduate Medical Education: A Systematic Review. Academic Pediatrics. 2022;22(1):21-8.
Abstract As an important manifestation of the current development and transformation of the world’s power and energy industries, the virtual power plant is an important foundation for optimizing the layout of energy resources. However, since there are many open channels in the virtual power plant, adversaries can implement eavesdropping, replay, impersonation, forgery, and other attacks to access the virtual power plant, and even publish false data in the virtual power plant to disrupt the operation of the virtual power plant. In addition, it is easy for an adversary to deduce key information such as the layout of virtual power plant equipment through the identity of the device. In this context, to ensure the security and privacy of devices when accessing the platform, in this paper, we propose an efficient authentication protocol based on the elliptic curve cryptography and zero-knowledge proof, which requires only two information exchanges. Security analysis shows that the proposed protocol can meet security features such as mutual authentication, key agreement, perfect forward secrecy, and device anonymity. Performance analysis indicates that the proposed protocol achieves a reasonable balance between computational and signaling overhead, and it is more suitable for achieving efficient device authentication and privacy protection in virtual power plants.
This article delves into the inherent security of blockchain technology by evaluating the sophisticated techniques it employs. Key among these are mathematical hash functions, elliptic curve cryptography, and zero-knowledge proofs. Mathematical hash functions ensure that data stored is immutable; any slight alteration to the information will lead to a drastically different hash output, making any tampering evident. Elliptic curve cryptography provides a robust encryption mechanism, ensuring that data transactions remain confidential and secure. Meanwhile, zero-knowledge proofs enable one party to prove to another that they possess specific knowledge without revealing the actual information, further bolstering privacy. Owing to these technological underpinnings, blockchain not only excels in safeguarding sensitive data but also facilitates operations like verifying information authenticity. Moreover, in sectors like supply chain management, it offers capabilities for precise logistics positioning and traceability. Such applications underline blockchain’s potential as a tool for transparency and security in various industries. Through these features and mechanisms, blockchain stands as an exemplar of digital security in today’s interconnected era.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
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.
I present an overview of the field of zero knowledge proof systems, which has evolved considerably over the last four decades, with applications ranging from secure authentication, secure communications, crypto-currency, and online privacy.
As a distributed ledger technology, blockchain has broad applications in many areas such as finance, agriculture, and contract signing due to its advantages of being tamperproof and difficult to forge. However, the open nature of blockchain also introduces severe privacy issues, currently cryptocurrency privacy protection solutions under account-based models cannot balance the internal verification time and confidentiality of transactions. In order to improve the confidentiality and efficiency of transactions under the account-based model, this paper proposes an access control anonymous payment scheme based on homomorphic commitment and aggregated zero-knowledge proofs, focusing on the realization of a one-to-many anonymous transfer function, one-to-one transfer function, deposit function and withdraw function, to ensure the privacy of a transaction data while reducing verification time and Gas costs. We evaluated our approach on a proof-of-concept implementation by generating Solidity contracts and implemented some interesting contracts. The experimental results show that this scheme not only consumes lower gas, but also reduces the internal time during transaction generation. In addition, our solution has established a one to many transfer transaction scheme, and the proof verification time after aggregation is constant. Thus the efficiency of this scheme is particularly well suited for the digital finance scenarios.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
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.