Hang Liu, Ming Yang, Aotian Cai, Chenhao Wang · 5 authors
In recent years, with the increasing prevalence of online group chat applications, malicious information has been more easily disseminated on the internet. Asymmetric group message franking (AGMF) allows users to report received malicious messages to moderators, achieving content moderation in large-scale online end-to-end messaging systems. However, the state-of-the-art construction is built upon traditional public key cryptosystems, resulting in the complex certificate management problem. This paper systematically explores identity-based AGMF (IB-AGMF) to resolve this issue. Specifically, we first introduce a novel primitive called hash proof system-based anonymous identity-based key encapsulation mechanism supporting sigma protocol (HPS-AIB-KEMΣ) and present a practical construction based on DBDH assumption. After formalizing the concept and security notions of IB-AGMF, we propose the generic construction of IB-AGMF based on HPS-AIB-KEMΣand non-interactive zero knowledge proof system. Finally, we conduct comprehensive performance evaluations and comparisons to demonstrate the feasibility of IB-AGMF in group communication scenarios.
Xin Liu, Anyang Qi, Lanying Liang, Dan Luo · 10 authors
In computer vision, the intersection determination of polygonal areas is utilized to segment different regions in an image and assist in detecting the boundaries of the regions. Moreover, the secure computation of the intersection area of polygons can solve the private calculation of geometric problems in machine learning. A security protocol under the semi-honest model was designed for the problem of secure computation of the intersection area of two polygons. This protocol adopts a new coding method and the Paillier homomorphic encryption algorithm. Aiming at the malicious behaviors that malicious participants may carry out in the semi-honest protocol, a secure computation protocol for the intersection area of polygons under the malicious model was designed by using methods such as hash function, cut-and-choose and zero-knowledge proof. The security of this protocol was proved, and its computational complexity and communication complexity were analyzed. Compared with the existing schemes, it is more efficient.
This paper proposes DAPUR, a decentralized anonymous payment protocol that simultaneously achieves strong transaction privacy and regulatory compliance. Addressing the fundamental tension between confidentiality and oversight in cryptocurrency systems, we develop a novel cryptographic framework combining zero-knowledge proof with access-controlled encryption. The protocol enables end-to-end transaction privacy while permitting authorized regulators to audit transaction details through selective disclosure mechanisms. The system’s security is formally proven through a model establishing ledger indistinguishability, transaction non-malleability, and balance preservation. Experimental results demonstrate practical viability with sub-second verification times. DAPUR represents a significant advance in privacy-preserving payment systems, showing that regulatory compatibility can be achieved without compromising decentralization principles.
With the growing demand for secure medical data sharing and accurate insurance premium assessment, privacy, trust, and interoperability have become pressing challenges in healthcare digitalization. Existing solutions often suffer from centralized control, inflexible access policies, and weak privacy guarantees. To address these, we propose a blockchain-based, privacy-preserving scheme that integrates threshold proxy re-encryption and zero-knowledge proofs. Patients’ encrypted medical records are stored in private cloud environments, while access control is enforced through a threshold proxy re-encryption mechanism. Dynamic rekeying and delegation are supported through re-encryption key redistribution. For insurance verification, we generate zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) based on arithmetic circuits that represent the insurance policy requirements. These proofs are verified on-chain without revealing any underlying medical information. Security and performance analyses demonstrate the feasibility and efficiency of the proposed system. Experimental results show that our scheme supports sub-100 ms re-encryption cycles, reduces zero-knowledge proof generation time by over 90% compared to other zk-SNARK schemes, and imposes low on-chain computational overhead. These findings demonstrate the feasibility and potential suitability for real-world privacy-preserving medical data sharing and insurance evaluation.
Sachin Aralikatti, P. Susheelkumar Sreedharan, Akhila K M, J Mexlin · 6 authors
In contemporary democracies, the security, openness, and confidentiality of electronic voting systems are paramount. Due to concerns with scalability and voter privacy, blockchain technology is not yet ready for usage in national-scale elections, despite its immutability and auditability. Using the State Assembly Election as case study, this paper elaborate a e-voting framework that uses Zero-Knowledge Rollups (zk-Rollups) to guarantee scalable, privacy-preserving, and tamper-evident vote recording on the blockchain. Layer-2 zk-Rollup, which is integrated into the architecture with IoT and embedded systems, generates concise cryptographic proofs of vote validity without disclosing individual choices, offloading computationally intensive voting transactions. By validating these proofs on the blockchain, petrol costs are drastically reduced and throughput is greatly improved. Embedded systems also incorporate cryptographic primitives such as zk-SNARKs, Merkle trees, and homomorphic commitments to safeguard against double-voting, end-to-end verifiability, and voter anonymity. This framework provides a viable, secure, and future-proof solution for transparent and scalable e-voting in civic and organisational contexts, it offers a prototype implementation using Zero-Knowledge Rollups on Blockchain with IoT and embedded system integration.
The rise of collaborative AI, particularly in distributed Mixture-of-Experts (MoE) systems, has created a critical challenge: how to ensure trust and transparency when aggregating proprietary models from different providers. To address this, we introduce a novel cryptographic protocol ZQ-WMA that enables verifiable and privacy-preserving online learning. Our method integrates zero-knowledge proofs with a quantized version of the Weighted Majority Algorithm, allowing a central aggregator to publicly prove it is honestly combining expert advice and updating weights according to the agreed-upon rules, all without revealing any confidential model parameters.This approach ensures that expert contributions are evaluated fairly and protects valuable intellectual property. Our analysis reveals that the quantization necessary for the zero-knowledge proofs can counter-intuitively enhance prediction accuracy, a phenomenon we attribute to the maximal entropy random walks. Furthermore, our benchmarks demonstrate the efficiency of this method, showing proof generation complexity less than 10% of a standard SHA256 hash function, with O(1) proof size and verification time. This work provides a practical and scalable framework for building trustworthy collaborative AI systems.
This paper presents a secure aggregation system Armadillo that has disruptive resistance against adversarial clients, such that any coalition of malicious clients can affect the aggregation result only by misreporting their private inputs in a pre-defined legitimate range. Armadillo is designed for federated learning setting, where a single powerful server interacts with many weak clients iteratively to train models on client's private data. While a few prior works consider disruption resistance under such setting, for an aggregation on n clients they either require high cost per client (Chowdhury et al. CCS '22) or concretely many rounds that is logarithmic in n (Bell et al. USENIX Security '23). Although disruption resistance can be achieved generically with zero-knowledge proof techniques (which we also use in this paper), we realize an efficient system with two new designs: 1) a simple two-layer secure aggregation protocol that requires only simple arithmetic computation; 2) an agreement protocol that removes the effect of malicious clients from the aggregation with low round complexity. With these techniques, Armadillo runs in 3 rounds per aggregation (our round complexity is independent of n) with computationally lightweight server and clients.
With the rapid development of the Internet of Things (IoT), Location-Based Services (LBS) have been widely applied in smart transportation, mobile social networking, and urban sensing. However, the high sensitivity of precise location data makes it a primary source of privacy breaches. Existing privacy-preserving solutions—such as k-anonymity, differential privacy, homomorphic encryption, or decentralized architectures—though partially mitigating risks, still rely on trusted third parties for anonymous set generation, key management, or query scheduling, leading to single points of failure, centralized trust, and potential misuse. Even decentralized proposals struggle to balance service quality with strong privacy guarantees, efficient verification, and lightweight deployment. To address this, this paper proposes a lightweight blockchain-based decentralized LBS privacy-preserving framework. This solution eliminates trusted intermediaries: users locally generate privacy-constrained fuzzy regions and construct zero-knowledge proofs (ZKPs) to cryptographically verify their actual locations within these regions. The proofs are submitted to blockchain smart contracts for public verification; only upon successful validation do distributed LBS nodes respond with candidate results, which are finalized through local user filtering. Theoretical analysis and experiments demonstrate that our framework effectively resists privacy inference from semi-honest service providers and external attackers, achieving a balance among query accuracy, response latency, and computational overhead. This provides a viable path for building secure, efficient, and user-centric LBS systems.
Zero-knowledge proofs (ZKPs) have been used to protect the integrity of machine learning (ML) models. However, existing ZKPs for ML are still inefficient, mainly due to the computational cost of evaluating non-linear functions. In this paper, we propose a ZKP framework for typical non-linear functions in ML, including Sigmoid, Softmax, etc. Compared to the state-of-the-art Hao et al. (USENIX Security ’24), our protocols obtain 115.6-2384.4× and 296.8-4104.7× runtime improvements for prover and verifier, respectively, with a 37.91269.5× reduction in proof size.
This work proves a formal impossibility theorem stating that no observable behavioral or biometric signal can serve as a cryptographic secret under standard semantic security notions (IND-CPA / IND-CCA), in any computational model admitting machine learning approximation and side-channel observability. The result holds in classical, post-quantum, and hybrid adversarial models. We further derive strict architectural consequences for biometric authentication, fuzzy extractors, and behavioral identification systems, showing that such signals may only function as zero-knowledge liveness proofs, not as entropy sources for cryptographic key material.
R. Krishnan, A.G. Samuelson, Emily Yao, Ethan Cecchetti
Non-Interactive Zero Knowledge (NIZK) proofs, such as zkSNARKS, let one prove knowledge of private data without revealing it or interacting with a verifier. While existing tooling focuses on specifying the predicate to be proven, real-world applications optimize predicate definitions to minimize proof generation overhead, but must correspondingly transform predicate inputs. Implementing these two steps separately duplicates logic that must precisely match to avoid catastrophic security flaws. We address this shortcoming with zkStruDul, a language that unifies input transformations and predicate definitions into a single combined abstraction from which a compiler can project both procedures, eliminating duplicate code and problematic mismatches. zkStruDul provides a high-level abstraction to layer on top of existing NIZK technology and supports important features like recursive proofs. We provide a source-level semantics and prove its behavior is identical to the projected semantics, allowing straightforward standard reasoning.
<p>Document forgery remains a pervasive problem across education, government, and trade sectors. This paper presents a blockchain-based digital document verification system built on the Internet Computer Protocol (ICP). The approach computes SHA‑256 hashes of documents and anchors them to ICP canister smart contracts, ensuring integrity and non-repudiation without storing document contents. The system manages a registry of approved verifiers so that only trusted institutions can enroll documents. In evaluation with 15 documents (85–3025 KB) and five repeated trials per document, the prototype achieved an average verification time of 1.54 s and an accuracy of 99%. Compared with Ethereum-based baselines in prior work, the ICP-based design avoids gas fees and reduces verification latency. The proposed architecture supports future integration of zero-knowledge proofs (ZKP) to validate authenticity while preserving privacy.</p>
Open access
Blockchain Technology Applications and Security
Big Data and Digital Economy
Physical Unclonable Functions (PUFs) and Hardware Security
With the deep integration of 6G, the Internet of Things, and artificial intelligence, this paper proposes an intrusion detection and defense framework that combines robust AI kernel reconstruction, a cross-layer collaborative perception architecture, and a dynamic defense closed-loop mechanism to address advanced persistent threats and dynamically evolving attacks targeting next-generation consumer services. First, a lightweight detection model ATF-KDBC is designed based on adversarial training and online knowledge distillation. Gradient masking and noise injection are employed to enhance robustness against adversarial samples, while a drift-aware module enables adaptive optimization under concept drift scenarios. The model achieves accuracies of 99.25% and 99.84% on the NSL-KDD and IoT-23 hybrid datasets, respectively, and compresses the model size to 1.08 MB, representing a 97.6% reduction compared with the BERT teacher model. Second, a multidimensional attack chain analysis model is developed based on a STHGN. By integrating semantic, structural, and temporal features with a multi-head self-attention mechanism, the model enables cross-layer threat tracing and millisecond-level response, achieving an F1-score exceeding 97.0% on the DARPA dataset. Furthermore, this study explores the construction of a distributed CTIS network by integrating federated learning and blockchain technology. Zero-knowledge proofs are employed to ensure privacy preservation, while a Quality of Data and Quality of Model scoring mechanism enables efficient and precise deployment of defense strategies. Experimental results demonstrate that the proposed framework significantly outperforms traditional methods in terms of robustness, environmental adaptability, and computational efficiency, thereby providing both theoretical support and a technical pathway for enhancing the resilience and security of next-generation consumer services.
This research examines the role of decentralized cryptocurrencies in facilitating money laundering and the challenges they pose to Anti–Money Laundering (AML) regulations through literature review and regulatory analysis. The study reveals that the decentralized, anonymous, and borderless nature of cryptocurrency enables illicit activities via cryptocurrency ATMs, mixing services, and decentralized exchanges (DEXs). For over a decade, the same regulatory problems persist today as were present at the inception of cryptocurrencies. Current AML frameworks, such as the Bank Secrecy Act and the Money Laundering Control Act, are inadequate for this decentralized ecosystem. The analysis critiques the fragmented efforts of U.S. regulatory agencies, identifying enforcement gaps and inconsistencies. To address these vulnerabilities, the paper proposes three solutions: mandating privacy–preserving technologies like zero–knowledge proofs for mixing services, requiring decentralized identity solutions for cryptocurrency ATMs and DEXs, and enhancing public education on cryptocurrency risks and safe practices. The study concludes with an urgent call for comprehensive regulatory reforms and educational initiatives to balance innovation, privacy, and security while combating money laundering in the cryptocurrency sector.
In admissions and hiring, transcript verification often requires only a threshold decision, for example a grade point average of 3.0 or higher or a Python grade of B or better. However, prevailing workflows demand full transcript disclosure, creating privacy risks and evaluation bias. We present a deployable system that proves a chosen subject meets a required threshold without revealing the score or any other subjects. The system integrates Groth16 Succinct Noninteractive Arguments of Knowledge (SNARKs) with W3C Decentralized Identifiers (DID) and Verifiable Credentials (VC) in a four-service architecture for DID registration, university issuance, student proving, and third-party verification. Holder identity is enforced offcircuit via a DID based challenge-response protocol where the student signs the verifier's nonce and the verifier checks that the signer's DID matches the VC's subject identifier. The zero-knowledge (ZK) circuit proves only Merkle inclusion and threshold comparison. The university signs the transcript's Merkle root with an Ed25519 signature. The verifier validates this signature using a key obtained from the university domain or a trusted registry, never from the student. On standard development hardware, steady-state proving latency is between 0.66 and 1.10 seconds. The verifier learns only a pass/fail bit, enforcing data minimization. In our negative test suite, no false accepts were observed.
Marxel S. Abogado, Worasait Suwannik, Geoffrey A. Solano, Somchoke Ruengittinun
Consensus sequences are reconstructed representations of original DNA strands, generated by aligning and combining multiple fragmented reads. This paper presents zkConsensus, a privacy-preserving system that verifies the generation of consensus sequences without revealing sensitive genetic information with the use of Zero-Knowledge Proofs (ZKPs). Implemented in Circom language, the circuit validates three validation parts: the consistency between original and aligned reads, the correctness of alignment scores across all read pairs, and the support for the consensus bases through majority voting. The system takes as public inputs the original reads, their lengths, and an expected score, while treating the alignments, reverse complement indications, start positions, and final consensus as private.
In today's rapidly advancing healthcare landscape, integrating Artificial Intelligence (AI) and Machine Learning (ML) has the potential to significantly improve patient care and streamline medical processes. The utilization of confidential patient data to train and develop these technologies, however, raises significant concerns regarding authenticity, security, and privacy. In this study, we introduce MediChainAI, a safe and practical framework that allows patients full ownership over their own health data by integrating Self-Sovereign Identity (SSI), Blockchain, and sophisticated cryptography techniques. By clearly outlining the goals and parameters of this access, MediChainAI allows patients to safely and selectively share data with healthcare providers and researchers. While SSI guarantees that patients have ownership of their data, the framework uses Blockchain technology to keep things transparent and secure. Further, MediChainAI makes use of Merkle trees, which provide verified access to subsets of data without jeopardizing the privacy of the whole dataset. The encryption mechanism, which is based on smart contracts, is a distinctive feature of the framework that allows researchers and medical practitioners controlled and secure access to patient data. In order to improve the accuracy and reliability of medical diagnoses and treatment, this strategy makes sure that only confirmed, legitimate data is utilized to train medical models. A significant step toward safer and more personalized healthcare, MediChainAI encourages ethical and patient-focused innovation by effectively resolving essential issues regarding data security and patient privacy.
Open access
2 source records
Blockchain Technology Applications and Security
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
Secure Multi-party Computation (MPC) considers the problem where a set of mutually distrusting parties want to jointly compute a function over their private inputs, without revealing any extra information about these inputs other than what it can be inferred from the output of the function. This setting is well-motivated, and it has many real-world applications such as auction, voting, etc. MPC can be also seen as a generalization of many natural cryptographic primitives. For example, zero-knowledge (ZK) can be viewed as a special case of two-party secure computation. In ZK, a party, called prover aims to convince a second party, called verifier, that the prover’s private input witness w and a public input statement x belong to a relation R. An important research direction in secure computation is to find the trade-off between the required setup (e.g., the use of the broadcast channel, the use of common reference string (CRS) / public key infrastructure (PKI), the upper bound of the parties that can be corrupted, etc.), and the security guarantees that can be achieved. The setups can be viewed as some general assumptions that the protocol needs to satisfy, and they influence the usability of the protocol in real-world scenarios. In principle, having simpler (or no) setups mean that the protocol is more general and can be more useful in real-world scenarios. At the same time, having simpler setups may lead to weaker security guarantees. Therefore, finding the trade-off between setup and security guarantees is important and meaningful. In this thesis, we target MPC and ZK, and we focus on how to minimize the setup for MPC and ZK while still providing meaningful levels of security. More specifically: Regarding MPC, we focus on the dishonest majority (i.e., the adversary can corrupt all but one party), and we aim at 1) minimizing the use of broadcast channels. 2) studying the MPC with pre-processing when no setup is available. • Informally, a broadcast channel guarantees that when a message is sent, this reaches all the parties, without ambiguity. It also guarantees that if an honest party receives a message from a corrupted party, then it is guaranteed that all the honest parties have received the message. To realize broadcast, parties in the protocol could run the broadcast protocol, which may require many rounds of peer-to-peer communications. An alternative way is to rely on physical or external infrastructure such as blockchain. In both cases, broadcast is expensive, as such, we want to minimize its use. In particular, this thesis presents the following results: – When assuming no setup, we give a complete characterization with respect to the use of broadcast channels, and we obtain the optimal results. – We consider the same problem for the case that we only want to allow the black-box use (i.e., do not have access to the code of the algorithm) of the oblivious transfer protocol. We also give a characterization. • In the standard definition of MPC, the parties’ private inputs are fixed before the start of the protocol. However, there is another type of MPC named MPC with pre-processing, where the protocol can pre-compute some messages without using parties’ inputs, and these messages can accelerate computations in the online phase (i.e., other computations that require parties’ inputs). Since some expensive computations can be pre-computed, the online phase could be more lightweight. Therefore, we want to remove the dependency of the input from as many rounds as possible, so that we can do some pre-processing. In this direction, we explore the protocol with no setup. We provide a compiler that can turn a big class of MPC protocol that may require the inputs already to compute the first round, into a new protocol that needs the inputs only in the last two rounds. We also propose new MPC definitions that capture this delayed-input features. Regarding ZK, we do the following: • In standard single-theorem ZK definition, the security of the ZK protocol is guaranteed to hold only when one proof is issued. In the case where multiple zero-knowledge proofs need to be issued (i.e., to prove multiple NP statements), each new zero-knowledge proof requires a freshly generated setup. In the multi-theorem ZK definition, instead, one setup is sufficient for generating multiple zero-knowledge proofs for multiple instances. We propose a multi-theorem protocol (in the format of a compiler) that follows the Fiat-Shamir paradigm and relies on correlation intractable hash functions. Moreover, our protocol remains zero-knowledge and sound even against adversaries that choose the statement to be proven (and the witness for the case of zero-knowledge) adaptively on the key of the hash function. Prior works could achieve this adaptive security only inefficiently via NP reductions. • ZK protocols are secure only when all setups are correctly generated, but in real-world scenarios, some of the setups may not be correctly generated. For instance, to run a non-interactive zero-knowledge (NIZK) protocol, the setup CRS could be chosen with bias. In this case, the security of the NIZK protocol does not hold anymore. Instead of finding a secure ZK candidate, one alternative solution is to have multiple instantiations of ZK candidates and assume that only for a subset of them the setup is generated correctly. More formally, we consider the case where only a subset of the instances are secure. In more detail, given access to n candidate instantiations of a NIZK for some language, we want to have a construction that itself implements a NIZK for the same language without relying on any additional computational assumptions. We refer to this type of construction as combiner, and the combiner is secure assuming at least t of the given candidates are secure. In this work, we provide three different constructions of robust NIZK combiners and show that combiners are impossible to realize unless the majority of the input candidates are secure.
This paper presents a novel hybrid cryptographic architecture for blockchain-based document signing that addresses fundamental interoperability challenges across heterogeneous blockchain platforms. The proposed system introduces technical innovations including a unified cryptographic protocol combining classical ECDSA signatures with post-quantum Dilithium algorithms, biometric authentication mechanisms, and zero-knowledge proof systems. The architecture enables seamless document signing across Ethereum, Hyperledger Fabric, and Solana networks through standardized smart contract interfaces and novel cross-chain verification protocols. Key technical contributions include mathematical formalization of hybrid signature schemes, implementation of privacy-preserving verification using NIZK proofs, and design of platform-agnostic verification mechanisms that maintain cryptographic security across different consensus architectures while achieving sub-second verification times.
(scroll down for English) Die GAIA Ökonomie – Kurze Gesamtdarstellung Falls Sie die PDF Dateien lesen, beginnen Sie bitte mit "Das Gaia System Buch". Das Buch diagnostiziert zunächst die strukturellen Grenzen des heutigen Geld- und Wirtschaftssystems: Zins- und Zinseszinseffekte verschieben Vermögen automatisch nach oben und erzeugen permanenten Wachstums- und Rationalisierungsdruck – mit sozialen, ökologischen und psychologischen Folgekosten. Reformschnipsel lindern Symptome, ändern aber nicht die Logik des Systems (Wachstumszwang, Hortungsanreiz). Eine anschauliche Metapher des „unsichtbaren Staubsaugers“ illustriert, wie Zinsmechaniken Jahr für Jahr Kaufkraft von vielen zu wenigen absaugen. Als Antwort entwirft GAIA eine neue ökonomische Infrastruktur aus zwei bewusst getrennten Modulen: (1) GAIA Coin als neutrales, nicht-spekulatives Zahlungssystem und (2) ein Impact-Modul als freiwillige Incentive-Schicht. Der Coin verankert einen leichten Umlaufdruck (Demurrage) im Code, damit Geld im Fluss bleibt und Horten unattraktiv wird („Fließen statt Horten“). Er dient nur als Tauschmittel (kein Vermögensvehikel), zielt auf Vermeidung von Vermögenskonzentration und entkoppelt Geld von Machtakkumulation. Technisch setzt das Zahlungssystem auf Dezentralität, Energieeffizienz und Datenschutz: pseudonyme Wallets und Zero-Knowledge-Proofs ermöglichen gültige Transaktionen ohne Offenlegung persönlicher Details; leichte Clients und energiearme Konsensverfahren sichern Alltagstauglichkeit; Open-Source-Code und Supermehrheit für Updates verhindern Machtmissbrauch. Zahlungen erfolgen in Sekunden, auch offline per QR/NFC möglich. Das Impact-Modul macht gesellschaftlich nützliche Wirkungen sichtbar und belohnbar – ohne Zwang und ohne Strafen. Es bewertet Beiträge zu Fürsorge, Bildung, Kultur, Ökologie oder Gemeinschaft mit Impact-Punkten, die den individuellen Coin-Wert leicht erhöhen. Die Bewertung kombiniert drei Säulen: Impact Smart Contracts (regelgebundene, transparente Prüfregeln), einen dezentralen Vertrauensgraphen aus validierenden Instanzen (z. B. NGOs, Universitäten, Bürgergremien) mit Reputation, sowie KI-gestützte Datenanalysen zur Mustererkennung und Manipulationsabwehr; zusätzlich schützen ZK-Nachweise die Privatsphäre. Für Governance und Schutz sorgen Gemeinwohlräte als dezentral organisierte, rotierende Kontrollinstanzen mit transparenter Arbeitsweise und Whistleblower-Schutz. Die Mitglieder erhalten eine anspruchsvolle Ausbildung (Rechtsstaat, Urteilsfähigkeit, Psychologie inkl. Gruppendynamik/Narzissmus/Trauma, technische GAIA-Grundlagen); Inhalte werden durch unabhängige Institutionen wie DGVT oder Alfred-Adler-Institut qualitätsgesichert. Grundlegende Systemänderungen bedürfen stets einer überwältigenden Supermehrheit. Die Einführung erfolgt politisch evolutionär statt revolutionär: Start in lokalen Pilotkreisen (Kommunen, Bürgerhaushalte, Genossenschaften), anschließende kommunale und regionale Integration; das bestehende Währungssystem bleibt parallel bestehen. GAIA wird als ergänzende Infrastruktur positioniert – anschlussfähig für unterschiedliche politische Lager – und perspektivisch durch klare Rechtsrahmen abgesichert (bis hin zur internationalen Non-Profit-Struktur bzw. zum öffentlichen Gut). Ein Schwerpunkt liegt auf Demografie: GAIA adressiert die Ursachen extremer Geburtenraten (Unsicherheit, fehlende Absicherung, Vereinbarkeitsprobleme) statt Symptome. Durch soziale Stabilität, Sichtbarmachung von Fürsorge und Förderung von Bildung/Familienplanung entsteht ein Balance-Effekt – in Regionen mit hoher Geburtenrate sinkt diese freiwillig; in alternden Gesellschaften wird Familiengründung wieder attraktiver. Konkrete Regionalkapitel (Afrika, Europa, Asien) illustrieren Wirkpfade und Folgewirkungen (Ressourcenschonung, weniger Not-Migration, sozialer Frieden). In Summe bietet das Buch einen juristisch-technischen und psychologischen Gesamtentwurf samt Anhang (Glossar, technische Spezifikationen, juristische Rahmenentwürfe) und einen praxisnahen Realisierungsplan für ein sofort pilotierbares System. Die Vision gilt als realistisch, weil GAIA auf Anreize statt Zwang setzt, technisch sicher und dezentral ist und von unten wachsen kann – mit spürbaren Vorteilen: mehr soziale Sicherheit, weniger Ungleichheit, wirksamer Umweltschutz. ______________________ VorwortWir sind sterbliche Zeugen einer verletzten Erde – und zugleich Träger einer unverletzlichen Würde. GAIA entsteht aus dieser Spannung: aus der Würde jedes Menschen und aus der Einsicht, dass Heilung dort geschieht, wo wir einander nicht verurteilen, sondern verstehen. Der Mensch ist im Innersten gut. Was wir „Böses“ nennen, wächst aus feindseligen Umfeldern, aus frühen und späten Traumata, aus unbewussten Abwehrmechanismen, die sich als irrationales Handeln zeigen. Darum fragt GAIA: Woher kommt jemand? In welchem Umfeld hat er gelernt zu denken? Wo liegen Brüche und Abspaltungen? Und wie führen wir Schritt für Schritt zu Gesundheit, Einsicht und Würde zurück? Diese Haltung ist kein Dekor, sondern das Fundament: geprägt von meinem juristischen Studium – mit seinem Sinn für Klarheit, Logik, Rechtsphilosophie und Verantwortung – und von den Einsichten Eugen Drewermanns: Menschen kann man nur durch Liebe heilen. Kein Mensch verliert seine unverletzliche Würde. Ein System, das diesem Menschenbild gerecht werden will, muss Strukturen schaffen, die Zugehörigkeit ermöglichen, Angst reduzieren und die innere Güte aktivieren – nicht durch Beschämung, sondern durch Verstehen und Verlässlichkeit. Ich veröffentliche die folgenden Texte bewusst früh und unpoliert, als fortlaufenden Dialog aus Fragen und Antworten zwischen mir und ChatGPT. Nicht, weil Form unwichtig wäre, sondern weil die Zeit drängt. Es geht nicht nur um fortschreitende Umweltzerstörung. Die Schere zwischen Arm und Reich öffnet sich zunehmend – mit exponientieller Dynamik. Menschen arbeiten immer mehr für immer weniger reale Kaufkraft. Öffentliches Eigentum wird privatisiert und verscherbelt. Staaten verschulden sich, geraten in Abhängigkeiten und tun, was Finanzakteure ihnen diktieren; Politik wirkt entmachtet, Wahlen scheinen kaum noch Kurswechsel zu bewirken. Gleichzeitig entstehen Konflikte – bis hin zu Kriegen – um Ressourcen und um wirtschaftliche Vorherrschaft. In dieser Lage ist Abwarten keine Option. Ich stehe öffentlich dazu, dass hier künstliche Intelligenz mitgewirkt hat; die Verantwortung für Auswahl, Bewertung und Veröffentlichung trage ich. Diese Rohform ist Absicht: Sie macht Herleitungen sichtbar, dokumentiert Entscheidungen und erlaubt, den Denkweg nachzuvollziehen. Redaktionelle Glättungen können später folgen – vorrangig ist die Umsetzung. GAIA ist ausdrücklich kein Projekt des Klassenkampfes. Es will die Gefühle, Anreize und Motivationen aller berücksichtigen – der Reichen, des Mittelstands und der Armen. Ziel ist Ausgleich statt Frontbildung, Würde, Sicherheit und Sinn für alle. Ich bin überzeugt, dass das möglich ist. Ein zentraler Mechanismus ist dabei die Demurrage (Umlaufsicherungsgebühr): Dadurch muss Geld für das Gemeinwohl nicht erst über Steuern „zurückgeholt“ werden, sondern es entsteht ein kontinuierlicher Finanzstrom, der unmittelbar ins Gemeinwohl fließen oder gemeinwohlfördernde Investitionen gezielt subventionieren kann. Technisch gilt: Jeder Nutzer entrichtet automatisch eine kleine Umlaufgebühr (z. B. 0,5 % pro Monat). Diese Beträge werden nicht vernichtet, sondern in einem Treasury (Schatzkonto) des GAIA-Coin-Moduls gesammelt. Das Treasury liegt bewusst im Coin-Modul, damit der Geldkreislauf sauber bleibt und jederzeit exakt nachvollziehbar ist, wieviel Demurrage eingenommen wurde. Aus diesem Topf werden allgemeine Ausgaben (Betrieb, Sicherheit, Technik) gedeckt und gemeinwohlförderliche Maßnahmen finanziert; das Impact-Modul entscheidet lediglich über die Vergabe aus diesem Treasury – einschließlich möglicher Demurrage-Rabatte – nach transparenten Gemeinwohl-Kriterien. Kurz: Demurrage-Einnahmen fließen ins Treasury des Coin-Moduls, das Impact-Modul verteilt daraus wirksam und nachvollziehbar. Ich weiß nicht, ob dieses System in seiner heutigen Form vollständig funktionieren wird. Wenn nicht, ändern wir es, bis es trägt. Und falls Teile scheitern, bin ich mir sicher: In diesen Ideen liegt genügend Inspiration und Struktur, um gemeinsam die Version zu finden, die funktioniert. GAIA ist als lernendes System gedacht – Fehler sind änderbare Daten. Statt Perfektion auf Papier beginnt nun die Programmierung: GAIA Coin und Impact-Modul, iterativ, testbar, offen. Bauen, prüfen, verbessern – jetzt. Wer mitgehen will, ist eingeladen; wer zweifelt, kann uns beim Beweisen zusehen. GAIA ist kein Schaukasten, sondern eine Baustelle für Gemeinwohl, Gerechtigkeit und Umweltschutz. Helme auf. An die Arbeit.E-Mail: info@dzydent.com _____________________ Executive Summary des Manuskripts „Das GAIA-System – Gesamtdarstellung“ (für Entscheider in Politik, Verwaltung, Technik und Stiftungen): Ausgangslage & ZielDas Buch reagiert auf strukturelle Fehlanreize des bestehenden Geldsystems (Horten, Vermögenskonzentration, Wachstumsdruck) und skizziert eine praxistaugliche, rechts- und techniknahe Alternative: GAIA als ergänzende Infrastruktur, die freiwillig parallel läuft und ohne Systembruch eingeführt werden kann. Kernlösung in zwei Modulen GAIA Coin (Zahlungsmittel): nicht-spekulativ, mit leichtem Umlaufdruck (Demurrage) im Protokoll verankert, um Geldfluss zu sichern und Hortung unattraktiv zu machen. Architektur: energiearme Konsensverfahren, pseudonyme Wallets, Zero-Knowledge-Proofs (ZKPs) für Datenschutz, offene Codebasis, Supermehrheit + Timelock für Änderungen. Zahlungen in Sekunden; Offline-Weitergabe per QR/NFC möglich. Impact-Modul (opt-in): freiwillige Wirkungsschicht, die gemeinwohlfördernde Handlungen (z. B. Fürsorge, Bildung, Kultur, Umweltschutz) erfasst und mit Impact-Punkten belohnt; keine Überwachung, keine Ideologie, s
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Artificial intelligence and the quick development of photograph editing software in latest years have made it very simple to regulate virtual pix covertly. The authenticity and dependability of digital media utilized in social networks, journalism, and criminal proof have come below scrutiny because of manipulations like copy-circulate forgery and deepfake creation. The aim of this work is to perceive photograph forgeries via combining deep gaining knowledge of-based class techniques with traditional feature extraction methods.The cautioned device extracts precise neighborhood functions from input images the usage of the oriented speedy and turned around brief (ORB) algorithm. For powerful feature matching, 2-Nearest Neighbor (2NN) and Hierarchical Agglomerative Clustering (HAC) are then used. A Convolutional Neural community (CNN) model is trained to distinguish among authentic and manipulated photos by means of figuring out pixel-degree irregularities and texture changes if you want to growth type accuracy. examined on the publicly reachable MICC-F220 and MICC-F2000 datasets, the device outperforms baseline SVM strategies with a ninety% detection accuracy and a zero.1 false tremendous charge
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