Zahra Fathi, Amir Jahangard Rafsanjani, Fatemeh Habibi
In cyber threat information sharing, secure transfer and protecting privacy are very important. In this paper we solve these issues by suggesting a platform based on private permissioned Blockchain, which provides us with access control as well. The platform is called Anon-ISAC and is built on the Enhanced Privacy ID (EPID) zero-knowledge proof scheme. It makes use of permissioned Blockchain as a way to keep identity anonymous. Organizations can share their information on incidents or other artifacts among trusted parties, while they keep their identity hidden. This will save them from unwanted consequences of exposure of sensitive security information.
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Junwei Zhang, Fan Yang, Zhuo Ma, Zhuzhu Wang · 6 authors
With the rapid development of Internet of Vehicles (IoV), vehicle-based spatial crowdsourcing (SC) applications have been proposed and widely applied to various fields. However, location privacy leakage is a serious issue in spatial crowdsourcing because workers who participate in a crowdsourcing task are required to upload their driving locations. In this paper, we propose a decentralized location privacy-preserving SC for IoV, which allows vehicle users to securely participate in SC with ensuring the task's location policy privacy and providing multi-level privacy preservation for workers' locations. Specifically, we introduce blockchain technology into SC, which can eliminate the control of vehicle user data by SC-server. We combine the additively homomorphic encryption and circle-based location verification to ensure the confidentiality of task's location policy. To achieve multi-level privacy preservation for workers' driving locations, we only reveal a grid where workers are located in. The size of the grid represents the level of privacy preservation. We leverage the order-preserving encryption and non-interactive zero-knowledge proof to prevent workers from illegally obtaining rewards by forging their driving locations. The security analysis results show that our framework can satisfy the above requirements. In addition, the experiment results demonstrate that our framework is efficient and feasible in practice.
Abstract Currently, attribute-based authentication provides a feasible solution for fine-grained access control in cloud environment. However, the existing schemes can not solve the following problems at the same time, that is, how to ensure that the computation cost of the client does not depend on the size of underlying access structure, and how to introduce distributed authorities to manage and maintain the attribute universe. To solve the above problems, an efficient multi-authority attribute-based authentication scheme is proposed. The new scheme uses the technique of distributed attribute-based encryption to realize the access control of anonymous users, and reduces users’ computation burden by optimizing the standard implementation zero-knowledge proof and outsourcing users’ computing tasks in the authentication stage. Under the new definition of security, it can be proved that the new scheme is secure and satisfies many attractive properties, such as introducing distributed authorities, supporting outsourcing computation, satisfying attribute anonymity.
Picnic is a post-quantum digital signature scheme, where the security is based on the difficulty of inverting a symmetric block cipher and zero-knowledge proofs. However, generating a Picnic signature to a specific message requires up to 300 kB content depending RAM. As the memory of an IoT device is limited this can lead to issues at the implementation. Our target is bringing post-quantum cryptography to IoT systems. We propose three structural adjustments of the Picnic algorithm to reduce the memory usage. Two adjustments are compatible with the reference implementation, one of them breaks backward compatibility. We show analytically that the content depending memory for generating a signature can be decreased to under 10 kB.With these adjustments, Picnic becomes suitable for IoT devices with little RAM. Since our approach also aims at easier parallelization, a speed-up depending on the number of instances is possible.
Recently, smart-parking systems were proposed employing blockchain technology to ensure security, transparency, and availability of services. Building on a principle of complete system decentralization, we present the concept and implementation of a privacy-preserving solution for renting parking spaces. The anonymity of users is ensured by a combination of zero knowledge proofs, commitment schemes, differential privacy and fungible tokens. We compare implementations on three different public blockchain networks: Ethereum, EOS, and Tron. We analyze security and privacy of the proposed system and evaluate how ensuring anonymity affects user experience. We conduct user-oriented test to explore the performance (transaction time and cost) of the system in realistic situations. Results indicate that the proposed concept is feasible while employing current blockchain technology. EOS and Tron implementations currently provide performance that matches existing centralized systems. The performance of Ethereum implementation would probably not meet user expectations.
The one-out-of-many proof is a cryptographic zero-knowledge construction enabling the prover to demonstrate knowledge of a secret element among the given public list of cryptographic commitments opening to zero. This method is relying on standard Decisional Diffie-Hellman security assumptions and can result in efficient accountable ring signature schemes [4] and proofs of set memberships [5] with a signature size smaller than all existing alternative schemes relying on standard assumptions. This construction also serves as a fundamental building block for numerous recent blockchain privacy protocols including Anonymous Zether [1], [2], Zerocoin [3], Lelantus [11], Lelantus-MW [9], Triptych [14] and Triptych-2 [15]. In this work, we introduce a new method of instantiating one-out-of-many proofs which reduces the proof generation time by an order of magnitude. Our approach still results in shorter proofs comprised of only a logarithmic number of commitments and does not compromise the highly efficient batch verification properties endemic to the original construction.
Jianhong Zhang, Qijia Zhang, Shenglong Ji, Wenle Bai
As an emergent-architecture, mobile edge computing shifts cloud service to the edge of networks. It can satisfy several desirable characteristics for IoT systems. To reduce communication pressure from IoT devices, data aggregation is a good candidate. However, data processing in MEC may suffer from many challenges, such as unverifiability of aggregated data, privacy-violation and fault-tolerance. To address these challenges, we propose PVF-DA: privacy-preserving, verifiable and fault-tolerant data aggregation in MEC based on aggregator-oblivious encryption and zero-knowledge-proof. The proposed scheme can not only provide privacy protection of the reported data, but also resist the collusion between MEC server and corrupted IoT devices. Furthermore, the proposed scheme has two outstanding features: verifiability and strong fault-tolerance. Verifiability can make IoT device to verify whether the reported sensing data is correctly aggregated. Strong fault-tolerance makes the aggregator to compute an aggregate even if one or several IoTs fail to report their data. Finally, the detailed security proofs are shown that the proposed scheme can achieve security and privacy-preservation properties in MEC.
Ken Naganuma, Masayuki Yoshino, A. Inoue, Yukinori Matsuoka · 6 authors
In recent years, the zero-knowledge proof and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) have drawn significant attention as privacy-enhancing technologies in various domains, especially the cryptocurrency industry and verifiable computations. A post-quantum designated verifier type zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) for Boolean circuits was proposed by Gennaro et al. in ACM CCS `18. However, this scheme does not include arithmetic circuits. Furthermore, it is difficult to use it in various applications. Their paper described the construction of a post-quantum designated verifier zk-SNARK for arithmetic circuits from quadratic arithmetic programs (QAPs) as an open problem. Recently, Nitulescu proposed a post-quantum designated verifier zk-SNARK for arithmetic circuits using square arithmetic programs (SAPs), which are the special cases of QAPs.In this paper, we give another answer to this problem and propose a post-quantum designated verifier zk-SNARK scheme for arithmetic circuits using QAPs. Our proposal, which employs QAPs, the zero-knowledge proof comprises three learning with errors (LWE) ciphertexts. We implemented our proposed scheme and the other known schemes using the libsnark library. Our experimental results show that our scheme can generate a zero-knowledge proof, which is known as the bottleneck of zk-SNARK, for an arithmetic circuit that comprises 216gates in a processing time of only 50 s, which is approximately three times faster than that of the post-quantum zk-SNARKs by Gennaro et al. or two times faster than the one by Nitulescu.
Early in 2020, a deadly new virus emerged and suddenly seemed to threaten the world with a pandemic. Like the plague of times gone by. What exactly were the risks? How possibly severe? What were the appropriate precautions? What were potential solutions? Reports in the media varied. Sometimes they even conflicted. With life and death possibly at stake, how would an average citizen know which claims were trustworthy? The COVID-19 crisis has dramatically underscored the need for functional scientific literacy.Now that the initial shock has passed, we are well positioned to reflect on recent history. What information seemed persuasive, but was misleading or not true? What apparently unlikely claims later turned out to be reliable? What can we learn from experience about how to assess any particular scientific claim?The customary wisdom – this month's Sacred Bovine – is that we should judge the arguments ourselves. With hucksters and ideologues everywhere, isn't it best to think for yourself? Namely, if we can equip someone to reason scientifically, do we not help them achieve intellectual independence? According to this view, argumentation is central to all science education (see Allchin & Zemplén, forthcoming).Yet a conundrum emerges in pursuing this strategy. To fully assess an argument, you need the evidence. However, “cherry-picked” data or biased samples can be misleading. To know whether you have enough relevant information, you have to be an expert already. To interpret a statistical analysis, you first need to know if the appropriate statistical model was used. That requires expertise as well. To assess experimental results, you need to know if the methods were sound – for example, if all the appropriate controls were included. And that, too, requires an expert's background knowledge. A “simple” assessment of an argument seems to involve an extraordinary level of expertise. But, of course, that very deficit is why the nonexpert seeks an answer in the first place. That's the conundrum: can you assess an argument on its own merits without also possessing all the expertise needed to make it?Indeed, the theme of expertise vs. argument reappears in the most prominent questions about the coronavirus. Consider a few examples – organized here in the form of an inquiry lesson, using history as data instead of students' own laboratory results (see → for questions to pose for student discussion).When the pandemic first threatened, shoppers soon emptied store shelves of hand sanitizer and face masks. No one needed much science to spur people's desire to protect themselves from possible harm. In that environment of fear, many websites (and a televangelist) offered products with the prospect of protections and cures. “VitalSilver,” a colloidal silver solution. Elderberry tincture. Boneset tea. Oregano oil. Antiviral essential oil aromatherapy. Frankincense. →Were any of them effective? How would you know?One did not have to wait long for an answer. None of these treatments was approved by the U.S. Food and Drug Administration (FDA), and the government quickly stepped in (Brewster, 2020). In Britain, the market was flooded with face masks with purported N95 protective status. They were labeled with known brand names, logos, and certifications. However, many were counterfeits (Daragahi, 2020). In India, Rwanda, Kenya, and elsewhere, people were caught selling fake hand sanitizer. All false claims. And all outright fraud. Easily exposed, perhaps. But these “simple” cases indicate that science con-artists and bogus claims are everywhere (Sacred Bovines, Nov., 2012; Oct., 2018). In assessing scientific claims, honesty matters as much as the content of the argument.From a perspective of scientific literacy, fraud is not so easily dismissed as one might imagine. It poses a critical epistemic problem. Namely: →How do you detect fraud?Fraud does not announce itself. It is not part of the argument. One needs to attend to the context, not just the content of the claim. Who is the speaker? Why are they making the claim? Is there a conflict of interest? Attention must shift from directly assessing what is claimed to analyzing who makes the claim, and why. That involves evidence, too, but of a very different kind. It turns out that the evidence for social context is just as important for other scientific claims as well.Another element of context revealed by cases of fraud is the psychological status of the recipient. →Why – or when – do we trust others? How does trust about reliable information differ specifically from other forms of trust – about moral guidance or personal loyalty?Many factors contribute to our sense of trust. We tend to believe those who speak with confidence and self-assurance (whether what they say is ultimately true or not). Emotions matter, too. Fear, or a desire to believe in a certain outcome, can distort the judgment of otherwise reasonable people. In addition, we tend to trust friends and allies. Or those who share our beliefs, our sense of identity, or a common enemy. We empathize with those who suffer innocently. Our wariness is quieted and our confidence lifted by assurances of credibility (even if it is a lie), by appearance, by familiar contexts, or by the appearance of a consensus. All these increase the susceptibility to fraud and science con-artists (Sacred Bovines, Nov., 2012; Oct., 2018).Science advocates often remark how science is founded on skepticism – that is, viewing claims by others with a measure of doubt. But perhaps, given the emotions just noted, we should equally focus the skeptical attitude on ourselves. Our own psychological vulnerabilities may strongly shape what “arguments” or “evidence” we accept as adequate. We should examine our own motives critically.Cases of fraud, even if infrequent, thus provide an important lesson. The social and psychological context of any scientific claim is critically important to consider, especially in a social setting.Early on in the history of COVID-19, it seemed that there might be no need to worry about a global pandemic at all. Many civic leaders characterized the virus as nothing more serious than the seasonal flu, and they assured the public that things were “totally under control.” Some called it a hoax. In mid-February, after dozens of cases had been detected in the U.S., the president told governors, “I think it's going to work out fine. I think when we get into April, in the warmer weather, that has a very negative effect on that, and that type of a virus.” In late February, he commented that of 15 cases, “within a couple of days is going to be down to close to zero.” In Brazil, even as late as early May (when most states had already been under lockdown for weeks) the president portrayed media claims about the threat as irresponsibly overstated. Typically, we expect government officials to monitor and heed scientific advice. Was it prudent to believe their claims, here? In retrospect, we can clearly see the answer. The messages minimizing the risks were tragically misguided. The pandemic did indeed become very serious. But could anyone really have known that in advance? →From a historically situated perspective, what would have been an appropriate basis for belief? Expertise or argument?In retrospect, we can see that trust in the experts was warranted. Assess the people, not the arguments or whatever evidence you are given. Experts, even with very simple data about transmission and travel, can build models to anticipate how a disease will likely spread. So, as early as the first week of January, 2020, the World Health Organization (WHO) voiced alarm, and by the end of the month it had formally declared a “public health emergency of international concern.” (That deceptively modest phrase was the technical label for a truly significant threat, such as the episodes of Ebola, SARS, and MERS in recent years, each with hundreds of deaths.) But many elected public officials seemed to disagree. →Whose view should one accept? Who is really qualified to know? What is expertise?Of course, as noted above, we may be strongly inclined to believe those who agree with us politically. But this does not qualify those individuals scientifically. Rather, the trust one needs is epistemic. One needs people who understand disease transmission and epidemiology. In this case, experts from the U.S. Centers for Disease Control and Prevention (CDC), along with other recognized authorities from around the world, concurred with WHO. Again, as the history now confirms, they were the trustworthy voices. The moral? Expertise matters (Oreskes, 2019).Consider, then, new model projections that were announced March 22 indicating that deaths in Italy were peaking and that coronavirus cases in the U.S. would be dwindling much sooner than most health experts had reported (Guzman, 2020). Promising news! →Were the claims credible? On what basis?In this case, they came from Michael Levitt, a Nobel laureate at Stanford University. His credentials certainly seemed to reflect expertise. Weeks later, however, deaths in Italy were still climbing, and cases in the U.S. showed no signs of abating, even as governors extended sheltering policies well into May. Unfortunately, perhaps, Levitt did not have the relevant expertise. His Nobel Prize was in chemistry. He was recognized for modeling the molecular structure of proteins and nucleic acids, not for modeling pandemics. Levitt's original comments to the Los Angeles Times revealed, perhaps, a telltale bias: “What we need is to control the panic.” In the grand scheme, he urged, “we're going to be fine.” Levitt's models were never formally published, nor endorsed by health experts. One needs not just any expertise, but the relevant expertise (Oreskes, 2019).What about cures? One rural doctor reported remarkable results using a mixture of hydroxychloroquine (or HCQ, an antimalarial drug), azithromycin (an antibiotic), and zinc sulfate (Roose & Rosenberg, 2020). It was an inspirational story of scientific discovery: a modest local physician, coping with a sudden barrage of COVID-19 cases, tries an obscurely reported cure and finds that it seems to work miraculously on all his patients. That was the narrative that attracted the attention of the U.S. president, who on March 20 touted it during a nationally televised news briefing. While acknowledging the drug's unproven status, he said, “I feel good about it. And we're going to see. You're going to see soon enough.” Anthony Fauci, one of the nation's top officials on infectious diseases (with decades of experience), then observed that the evidence for the cure was “anecdotal” at best, and cautioned against unwarranted hope or action. Several news commentators took Fauci to task for challenging the president's authority. Despite Fauci's remarks, the president continued to repeat his claims for the next few weeks, portraying HCQ as potentially “one of the biggest game changers in the history of medicine” (Crowley et al., 2020; Reuters, 2020). Meanwhile, other medical researchers echoed Fauci's skeptical posture. Again: →What should one believe? Miracle cure or tantalizing hype? Argument or expertise?One might imagine, as a purported ideal, that an ambitious citizen would investigate and assess all the evidence on her own. This assumes, of course, that such a person could interpret all the subtleties of clinical trials. But that level of medical expertise and background is beyond even most well-educated consumers. For example, if you found one study reported in a French journal, would you appreciate its limitations, based on a meager sample size of 20? Also, there was no control group, to ensure that any observed effect was due to the drug, rather than exhibiting the normal course of patients in the sample (Sacred Bovines, May, 2020). Soon, other doctors in Paris could not replicate the results. Indeed, concerns surfaced about how this paper was reviewed and whether it met customary standards for publication (Retraction Watch, 2020). What about the New York doctor's study? Well, he reported his results in a video addressed directly to the president, later posted on YouTube. There were no formal records documenting the course of treatment. And again, no controls. Without systematic evidence, can one justify bold claims? Still, several clinical studies were promptly begun to address the question. In early April, however, a study with 81 patients in Manaus, Brazil, was halted when fatal heart complications developed among many patients (Thomas & Sheikh, 2020). Although the FDA had approved HCQ to treat other conditions, it is not safe for use in the recommended doses for COVID-19. In the meanwhile, the career professionals at CDC had removed comments about the prospective use of the drug from its website, restating its earlier position that “there are no drugs or other therapeutics approved by the US Food and Drug Administration to prevent or treat COVID-19” (Reuters, 2020). “Oversold false hope” seems to be the historical judgment on hydroxychloroquine.Of course, most ordinary people would not have access to the resources, nor devote the time, to analyze HCQ so thoroughly. Nor would such effort really be needed. As in the cases above, a claim's credibility is most directly and effectively established by expertise. Yet despite their professional status, we should not have trusted those two doctors. →Why not?Reliable medical knowledge is not established by one person or by a few, or by a few loose studies. Experts must also agree. The local doctor may have been an expert on practicing medicine. But he proved not to be an expert in medical research. The French doctor who led the now disputed study seems to have largely retreated from professional discourse since, to promote his cause on YouTube instead (Sayere, 2020). The consensus of experts is essential (Oreskes, 2019). In this case, the collective of informed, qualified experts never endorsed HCQ for COVID-19. As the history now bears out, that expert consensus was a sufficient basis for belief.Other contested claims about COVID-19 have entered public discourse. For example, the whether one should a face in Some it was a to the of the the time, a said, can do it. have to do it. I not to do it. a it's So, how important was For example, the of a from the but when would it be In 2020, that it would be “we're very historical in and given the when it be safe for to to to to and we expect a what would have been trustworthy at the to reliable from claims? 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In this issue of Cytometry A, Zhao et al. (page 1073–1080) report on their work to diagnose leukemic B cell non-Hodgkin's Lymphoma from flow cytometry (FCM) raw data of blood and bone marrow samples using a dedicated computer approach, which would assign one of eight B-cell lymphoma diagnoses or “normal” to a sample. A remarkable of level of classification performance could be achieved in the validation set. For the “true” classification of B-cell lymphomas, conventional diagnostics had incorporated morphology, FCM and additional information from histology and genetics if needed, whereas computer diagnosis was derived from FCM data alone. In this context, uncertainty to delineate, for example, monoclonal B-cell lymphocytosis from chronic lymphocytic leukemia or to subclassify a B-cell malignancy as either mantle cell lymphoma or prolymphocytic leukemia is not an outright error, but is rather based on the limitations of FCM itself. Furthermore, cell populations tagged as abnormal by the algorithm and color-coded accordingly in conventional plots can help human diagnosticians to review and fine-tune the diagnosis. However, some lymphomas (most prominent in follicular lymphoma) were classified as normal by the algorithm. Vice versa, only few samples classified as “normal” by human diagnosticians were classified as lymphoma by the algorithm. Thus, a deficit in sensitivity exists, which is clinically relevant. Computer support is instrumental for the analysis of FCM data, because nobody is able to draw conclusions from raw list mode files. However, conventional FCM computer programs execute relative simple tasks to support the workflow of a human researcher or diagnostician. In a typical workflow, several sequential steps have to be performed (Fig. 1, left side). Fluorescence spillover compensation is calculated from control samples. One-dimensional transformation of raw data (logarithmic, logical, possibly a shift of zero and negative values to some defined minimum, etc.) is routinely performed on fluorescence channels. Data are displayed in histograms or two-dimensional plots. Starting gates are used to look for artifacts and to remove debris and cells not of interest. A considerable number of plots are necessary, if several fluorochromes are used and several populations are of interest. Data from several samples with identical panel may be displayed in parallel in an overlay. Cells are tagged according to gates in these plots and may then be displayed separately and/or color-coded. Hierarchical and/or Boolean gating strategies are used for the definition of cell populations and subpopulations of interest. Cell numbers and antigen expression of these cell populations of interest constitute the readout of a single tube. A final result or diagnosis is derived assessing this readout or the synopsis of the readout of several tubes. All of the calculations in such a manual workflow are based on straight “if A then B” logic, performing calculations on a maximum of two parameters concurrently. Conventional FCM computer support aims at displaying data in a clear manner to the human operator, especially effects of manipulation in two-parameter plots upon plots of other parameters, but not at automation. The most advanced process in standard applications is the calculation of fluorescence spillover compensation, which nowadays usually is performed in some (semi-) automated fashion. However, although every single step in this procedure is quite straightforward, due to the multitude of plots and gates from current 10 to 14 parameter FCM data, important information may be missed. In the recent decades, many attempts have been reported to introduce more advanced computation methods into histology, cytopathology, image cytometry and conventional FCM analysis (1, 2). These algorithms will be called artificial intelligence (AI) from here, although some of them do not deserve this name in its strict sense. Two strategies, sometimes overlapping, are applied in these attempts: firstly, AI may be used to automatize conventional data processing and analysis as described above in order to reduce the workload for the investigator, reduce bias using standardized procedures, and speed up analyses. To this end, regarding FCM, algorithms search for minimal values in distributions to define optimal positions for gates to divide populations or search for appropriate cut off values to gate out debris. Furthermore, normalization algorithms can be applied to level out differences due to instrument settings or biological variations in sets of multiple similar data. Many of these algorithms are available in the Bioconductor “flow Core” FCM package implemented in R (3). Secondly, new methods were introduced that go beyond the sequential analysis of two-dimensional plots and base calculations on more parameters of the higher-dimensional space in parallel, which is a crucial need nowadays, when standard cytometers report 10 to 14 parameters per cell and dedicated research instruments up to over 100 parameters. Such algorithms can either substitute conventional strategies, for example, to gate cell populations and read out antigen expression levels or they can be used to extract information from the raw data that is not accessible by conventional gating (4). One of the prominent tasks within an FCM workflow is to define cell populations within a mixture of different cells (“clustering”) that may be of interest for research or diagnosis. AI can directly use higher dimensional data as input for cell clustering or it can perform dimensionality reduction and data visualization, for example, by tSNE or one of its variants (5, 6) or SOM (7), the latter already including some clustering of the data. After dimensionality reduction, population clustering can be added by separate AI algorithms or a human operator can take over for this task, integrating the output of the dimensionality reduction and conventional gating. Many different algorithms are able to solve the task of clustering in an automated fashion either performing a two-step procedure integrating dimension reduction and subsequential clustering or direct clustering of higher dimensional data; however, as shown in the FlowCAP challenges, results are not unequivocal, especially, if the number of clusters is not defined a priori, and differences remain between different algorithms and human experts. Up to now, no perfect automatic solution for cell clustering exists, although many solutions perform quite well (7). Furthermore, clustering revealing further information on relatedness between populations has been suggested for a multitude of different research questions, for example, cellular developmental trajectories, and has been optimized according to these special tasks (further Ref. in 4). Furthermore, metadata extracted from raw FCM data may also be clustered, for example, in order to define diagnostic or prognostic subgroups (8). Whereas unsupervised clustering can be helpful for many exploratory research questions to identify cell populations and subpopulations, for medical diagnostic purposes supervised AI methods have been described, that use external information such as diagnoses or outcome to train the AI, for example, using support vector machines or neural networks. All of these strategies rely on a large dataset for training and may incorporate more or less steps from a conventional workflow (4, 9, 10). Manual gating and tagging of cell populations may be used for training of the AI (11) or AI may be trained using only the final results, that is, diagnosis, as described, for example, in Ref. (12) or in the work by Zhao et al. discussed here. Several AI strategies have been able to discern overt acute myeloid leukemia (AML) from normal samples with a high success rate in the second FlowCap challenge (7), however, this can be a considered a quite simple task, since overt AML is easily characterized by a large abnormal population of blast or sometimes monocytic cells. In contrast, separation of AML from myelodysplastic syndromes or from acute lymphoblastic leukemia, everyday questions in diagnostics, is less trivial. In contrast to simplified “yes or no” tasks, Zhao et al. tackled a much more realistic question: to deduce a specific diagnosis from FCM panels as they are used in conventional diagnostics. They achieved this goal without an attempt to mimic a conventional human FCM workflow. They transformed the FCM data by self-organizing maps (SOM) and classified these representations by a convolutional neural network (CNN), dealing with each tube separately first and finally with data from all three tubes. The researchers took advantage of a very large database of patient sample FCM data. Data from more than 18,000 samples analyzed in a uniform fashion with identical antibody combinations and more than 200 samples of the rarest subtype of lymphoma could be used to train the CNN. In order to get some insight into the CNN “black box,” they checked, which markers were of most importance for the AI to classify a specific diagnosis correctly and they had cell populations tagged that were detected to be abnormal and discriminative by the algorithm for the respective disease in a way to understand the AI's decision (and to use this assignment for a possible refinement by a human diagnostician in practical diagnostic use in the future). As described above, the results of their approach are remarkable, but a problem in sensitivity to detect all true lymphoma cases remains, which is most prominent for follicular lymphoma. Maybe the CNN could be trained in a way, that the correct distinction B-NHL of any type versus normal is assigned a higher weight compared to B-NHL subtyping. If we inspect the importance of single markers for AI performance in Supporting Figure 5, we note that some diagnosis assignments rely heavily on a few markers, whereas other diagnoses seem to rather depend on the distribution of many markers. Interestingly, the latter diagnoses without dependence on dominant markers have the highest rate of falsely being categorized as normal (follicular lymphoma, marginal zone lymphoma, lymphoplasmactic lymphoma). Furthermore, for a human diagnostician, an imbalance of kappa versus lambda light chain expression on B cells is a very important clue for a diagnosis of B-cell lymphoma, whereas the CNN of Zhao et al. does not seem to rely heavily on this information. In a different approach, to detect minimal residual disease in childhood acute leukemia, conventional gating was used to train a machine learning algorithm based on Gaussian mixture models (11). Thus, for the non-AI expert the idea comes up, if some information of a conventional workflow, collected by an automated application, could be “injected” into a CNN algorithm. If we assume that the problem of sensitivity will be tackled by improved versions in the near future, the AI solution of Zhao et al. will in fact be able to perform at “hematologist-level” and may even deliver B-NHL subtyping competence exceeding the results of conventional FCM alone. However, further problems have to be solved for a broader uptake of such a method: different laboratories work with different antibody panels and even antibodies recognizing the same cluster of differentiation antigen behave differently due to different antibody clones, different fluorochromes and different spillover from other fluorochromes in the panel. Thus, some methods of knowledge transfer are needed, if we want to avoid starting again with a training sample of more than 10,000 cases for every new antibody panel. If researchers will be able to solve these problems, AI for diagnostic FCM may finally leave the “proof of concept” stage and enter routine diagnostics. Open access funding enabled and organized by Projekt DEAL.
Cherukupalli Veda Vyasa Aditya, Rajesh Kannan Megalingam
Zero knowledge proof is a powerful cryptographic protocol that is utilized to establish data security whilst ensuring and maintaining user anonymity. ZKP has relatively less complex computational requirements as compared to the other protocols for authentication. Conventional authentication schemes are susceptible to attacks such as MiTM, IP spoofing, DoS, replay and other eavesdropping based attacks, when the data is shared across an untrusted network. This paper shows an approach to ensure authentication of a device over an untrusted network whilst maintaining and safeguarding user credentials, by using the concepts of ZKP protocol.
Driverless parking, an influential application of Mobility as a Service (MaaS) model, is one of the clear early benefits for autonomous vehicles, given often narrow spaces and multiple potential hazards (such as pedestrians stepping out from in between other vehicles). In recent years, real momentum has been building up for designing automated parking models for vehicles. However, in such an autonomous parking design, location privacy and identity privacy issues are always overlapping due to the improper sharing of data. Most existing studies barely investigate and poorly address such privacy issues. Motivated by this, we develop (and evaluate) an experience-driven, secure and privacy-aware framework of parking reservations for automated cars. Our idea of using differential privacy with zero-knowledge proof provides both security and privacy guarantees to users. Furthermore, the performance of the developed model is enhanced by exploiting reinforcement learning approach such that the utility of the system and the parking reservation rate can be maximized. Extensive evaluation demonstrates the superiority of the proposed model.
A fundamental requirement for all democratic governments is a secure voting system.In recent years some countries, like Estonia and Switzerland, have adopted internet voting (i-voting) and many more have experimented (e.g., Norway and Australia) or have plans to adopt it in the future (e.g., Lithuania and Russia).Ivoting has the potential to offer better convenience, lower administrative costs, and higher voter turnout, but this comes with increased security concerns and significant technical challenges.Consider the simple procedure of shaking the ballot box to mix the order of the ballots.It is far from obvious how to achieve an equivalent result with encrypted digital ballots.Who should perform the mixing procedure?How to guarantee that it was performed correctly?Is it possible that no one can trace the ballots?This problem can be solved with a distributed system called a mix-network.The idea is to let each peer in the mix-network shuffle (permute and rerandomize) the ciphertexts.This makes it computationally hard to trace the input ciphertexts to the output ciphertexts given that at least one peer is honest.However, each peer should also give a proof that the shuffling was done correctly to avoid substitution attacks.The proof has to be hard to forge (sound) and should leak nothing but the truth of the statement (zero-knowledge).Such proofs are called zero-knowledge shuffle arguments, and in this thesis, we study their constructions.Importantly, we avoid the heuristic security model, used in many of the previous works, which (incorrectly) treats a cryptographic hash function as a truly random function.We show that it possible to construct shuffle arguments, that are efficient enough for large-scale elections, in other security models than the random oracle model.First, we construct a very efficient non-interactive shuffle argument that avoids the random oracle model and instead uses the generic group model.This is achieved by combining several recent tools like quasi-adaptive zero-knowledge arguments and SNARKs.We implement it and observe practical efficiency for large-scale elections: the proving time for 100,000 ciphertexts is less than a minute, and verification time is less than 1.5 minutes on modest hardware.Unfortunately, security requires that the prover and the verifier have access to a trusted common reference string (CRS).Secondly, we study how to reduce trust assumptions.There are efficient multiparty computation (MPC) protocols for generating CRSs for a large class of arguments, but they require the random oracle model.We improve upon one such protocol and, among other results, remove the requirement for the random oracle.We prove the security of this protocol in the universal composability setting.Thirdly, we modify our shuffle argument to be applicable to the above MPC protocol.This guarantees both soundness and zero-knowledge as long as at least one peer in the MPC protocol is honest.We go one step further and show how to get zero knowledge even if all the peers are malicious.Additionally, we simplify the argument construction and prove its security based on weaker assumptions.
Sylvain Chatel, Apostolos Pyrgelis, Juan Ramón Troncoso-Pastoriza, Jean‐Pierre Hubaux
In the digital era, users share their personal data with service providers to obtain some utility, e.g., access to high-quality services. Yet, the induced information flows raise privacy and integrity concerns. Consequently, cautious users may want to protect their privacy by minimizing the amount of information they disclose to curious service providers. Service providers are interested in verifying the integrity of the users' data to improve their services and obtain useful knowledge for their business. In this work, we present a generic solution to the trade-off between privacy, integrity, and utility, by achieving authenticity verification of data that has been encrypted for offloading to service providers. Based on lattice-based homomorphic encryption and commitments, as well as zero-knowledge proofs, our construction enables a service provider to process and reuse third-party signed data in a privacy-friendly manner with integrity guarantees. We evaluate our solution on different use cases such as smart-metering, disease susceptibility, and location-based activity tracking, thus showing its versatility. Our solution achieves broad generality, quantum-resistance, and relaxes some assumptions of state-of-the-art solutions without affecting performance.
Blockchain-based systems open new opportunities for the IT business and society if designed and implemented properly. One of the essential challenges on their way to production readiness are severe privacy concerns and hence stringent compliance regulations. In this paper, we investigate how different privacy-preserving techniques leveraging zero-knowledge proof protocols and anonymous credentials can be used to tackle this issue on the example of a car sharing use case. Our approach considers a blockchain ecosystem enabling different parties including governmental authorities and automobile industry partners to cooperate ensuring end user privacy protection. The implemented proof-of-concept is leveraging Ethereum ZoKrates and Indy technologies respectively. This project is a joint initiative of T-Systems Multimedia Solutions GmbH and BMW Group.
Abstract Round complexity is one of the fundamental problems in zero-knowledge (ZK) proof systems. Non-malleable zero-knowledge (NMZK) protocols are ZK protocols that provide security even when man-in-the-middle adversaries interact with a prover and a verifier simultaneously. It is known that the first constant-round public-coin NMZK arguments for NP can be constructed by assuming the existence of collision-resistant hash functions (Pass, R. and Rosen, A. (2005) New and Improved Constructions of Non-Malleable Cryptographic Protocols. In Gabow, H.N. and Fagin, R. (eds) Proc. 37th Annual ACM Symposium on Theory of Computing, Baltimore, MD, USA, May 2224, 2005, pp. 533542. ACM) and has relatively high round complexity; the first four-round private-coin NMZK arguments for NP can be constructed in the plain model by assuming the existence of one-way functions (Goyal, V., Richelson, S., Rosen, A. and Vald, M. (2014) An Algebraic Approach to Non-Malleability. In 55th IEEE Annual Symposium on Foundations of Computer Science, FOCS 2014, Philadelphia, PA, USA, October 1821, 2014, pp. 4150. IEEE Computer Society and Ciampi, M., Ostrovsky, R., Siniscalchi, L. and Visconti, I. (2017) Delayed-Input Non-Malleable Zero Knowledge and Multi-Party Coin Tossing in Four Rounds. In Kalai, Y. and Reyzin, L. (eds) Theory of Cryptography15th Int. Conf., TCC 2017. Lecture Notes in Computer Science, Baltimore, MD, USA, November 1215, 2017, Part I, Vol. 10677, pp. 711742. Springer). In this paper, we present a six-round public-coin NMZK argument of knowledge system assuming the existence of collision-resistant hash functions and a three-round private-coin NMZK argument system from multi-collision resistance of hash functions assumption in the keyless setting.
This paper will give both the necessary and sufficient conditions required to find a counter-example to the Goldbach Conjecture by using an algebraic approach where no knowledge of the gaps between prime numbers is needed. To eliminate ambiguity the set of natural numbers, $\mathbb{N}$, will include zero throughout this paper. Also, for any sufficiently large $a \in \mathbb{N}$ the set $\mathcal{P}$ is the set of all primes $p_i \leq a$. It will be shown there exists a counter-example to the Goldbach Conjecture, given by $2a$ where $a \in \mathbb{N}_{> 3}$, if and only if for each prime $p_i \in \mathcal{P}$ there exists some unique $q_i, \alpha_i \in \mathbb{N}$ where $a 3$. However, this leads to contradiction since $2a 4$.A similar method will be employed to give the necessary and sufficient conditions when an even number is not the difference of two primes with one prime being less than that even number. To begin, let $a \in \mathbb{N}_{> 3}$ with the condition that the function $\gamma(a + 1)$ is equal to one if $a + 1$ is prime and zero otherwise. $2a$ is a counter-example if and only if for each prime $p_i \in \mathcal{P}$ there exists some unique $u_i, \beta_i \in \mathbb{N}$ where $2a 3$ to the equation above, leading to the same contradiction as the Goldbach Conjecture since $2a 4$. These proofs will have implications for proving the Polignac Conjecture.
Activity-tracking applications and location-based services using short-range communication (SRC) techniques have been abruptly demanded in the COVID-19 pandemic, especially for automated contact tracing. The attention from both public and policy keeps raising on related practical problems, including \textit{1) how to protect data security and location privacy? 2) how to efficiently and dynamically deploy SRC Internet of Thing (IoT) witnesses to monitor large areas?} To answer these questions, in this paper, we propose a decentralized and permissionless blockchain protocol, named \textit{Bychain}. Specifically, 1) a privacy-preserving SRC protocol for activity-tracking and corresponding generalized block structure is developed, by connecting an interactive zero-knowledge proof protocol and the key escrow mechanism. As a result, connections between personal identity and the ownership of on-chain location information are decoupled. Meanwhile, the owner of the on-chain location data can still claim its ownership without revealing the private key to anyone else. 2) An artificial potential field-based incentive allocation mechanism is proposed to incentivize IoT witnesses to pursue the maximum monitoring coverage deployment. We implemented and evaluated the proposed blockchain protocol in the real-world using the Bluetooth 5.0. The storage, CPU utilization, power consumption, time delay, and security of each procedure and performance of activities are analyzed. The experiment and security analysis is shown to provide a real-world performance evaluation.
In a BlockChain environment, during validation process or address lookup user personal information may be disclosed or hacked. To prevent this loophole, a Financial Wallet system with authentication and data protection is implemented. In this system, to avoid the disclosure of personal data and to limit the third party intermediaries a BlockChain with Zero knowledge proof is incorporated. To this implementation AES cryptography method is used to secure data. To make it more confidential NFC cards are used to authenticate user instead of passwords.
Blockchain secures a variety of IoT circumstance, when information or system validation information is placed on a blockchain, personal data might be spilled through the affirmation of working system. This paper observes a Zero-Knowledge proof for an awesome meter framework to illustrate the effectiveness of uncovered information as an instance. This research work has pondered a way to enhance the obscurity of blockchain for safety insurance. [2] In addition to that, device Near-field communication (NFC) generation is used as a mobile platform application. The fundamental idea of this research is to provide a secure service to a consumer through mobile application using the near field communication card and Zero knowledge authentication system.
The bioinformatics features are collected by pattern recognition technology, and the digital coding and format conversion of the feature data are realized by using the theory of topological group transformation. Authentication and Signature based on Zero Knowledge Proof Technology can be used as the trusted credentials of cloud platform and cannot be forged, thus realizing trusted and secure access.
In the communication environment of smart homes, personal data, control messages, and sensitive data are transmitted through wireless sensor networks (WSNs). Therefore, to prevent an invasion of privacy, communication has to be encrypted, and the data have to be stored securely. In this paper, we propose a new secure privacy-preserving authentication scheme for smart homes. We propose the concept of non-interactive chaotic zero-knowledge proof (NCZKP) and use it for our scheme to resist ephemeral secrets leakage (ESL) impersonation attack, which assures that the adversary can extract the sensitive information stored in gateway note, and use it to impersonate as a legal user. Also, the formal security analysis Random-or-real model is used to prove that our scheme is secure against different known attacks. In the end, according to the experiment, our scheme has low computation and communication costs compare with other related schemes.
Vehicular Ad Hoc networks (VANETs) is a new emerging technology that aims at connecting vehicles using wireless communication technologies. With the emergence of VANETs, new advanced applications have emerged away which aim at enhancing driving safety and traffic management. These applications exploit the huge amount of data, shared between vehicles and infrastructure, through advanced data analysis. Due to resources limitation of vehicles, this increasing volume of data is stored on powerful edge computing servers spread over the VANETs infrastructure. However, these edge servers are not fully trusted, which rise new serious security and privacy challenges regarding the shared data between vehicles. In this work, we propose a new data-sharing scheme that protects the privacy of vehicles and drivers. We base our construction on consortium blockchain, smart contracts and Zero-Knowledge Proofs (ZKP) to propose a decentralized and anonymous data-sharing scheme. In addition, we propose a fine-grained data storage scheme on the top of blockchain, based on publish-subscribe model to enhance the data management. We confirm the efficiency of our scheme through extensive simulations and experiments. The numerical results showed that our protocol achieves a reasonable efficiency while guaranteeing a high level of security.