Practical Predicate Encryption for Inner Product

Inner product encryption is a powerful cryptographic primitive, where a private key and a ciphertext are both associated with a predicate vector and an attribute vector, respectively. A successful decryption requires the inner product of the predicate vector and the attribute vector to be zero. Most of the existing inner product encryption schemes suffer either long private key or heavy decryption cost. In this manuscript, an efficient inner product encryption is proposed. The length for a private key is only an element in $\mathbb{G}$ and an element in $\mathbb{Z}_p$. Besides, only one pairing computation is needed for decryption. Moreover, both formal security proof and implementation result are demonstrated in this manuscript. To the best of our knowledge, our scheme is the most efficient one in terms of the private key length and the number of pairings computation for decryption

Efficient and Secure Data Sharing Using Attribute-based Cryptography

La crescita incontrollata di dati prodotti da molte sorgenti, eterogenee e di- namiche, spinge molti possessori di tali dati a immagazzinarli su server nel cloud, anche al fine di condividerli con terze parti. La condivisione di dati su server (possibilmente) non fidati fonte di importanti e non banali questioni riguardanti sicurezza, privacy, confidenzialit e controllo degli accessi. Al fine di prevenire accessi incontrollati ai dati, una tipica soluzione consiste nel cifrare i dati stessi. Seguendo tale strada, la progettazione e la realizzazione di politiche di accesso ai dati cifrati da parte di terze parti (che possono avere differenti diritti sui dati stessi) un compito complesso, che impone la presenza di un controllore fidato delle politiche. Una possibile soluzione l\u2019impiego di un meccanismo per il controllo degli accessi basato su schemi di cifratura attribute-base (ABE ), che permette al possessore dei dati di cifrare i dati in funzione delle politiche di accesso dei dati stessi. Di contro, l\u2019adozione di tali meccanismi di controllo degli accessi presentano due problemi (i) privacy debole: le politiche di accesso sono pubbliche e (ii) inefficienza: le politiche di accesso sono statiche e una loro modifica richiede la ricifratura (o la cifratura multipla) di tutti i dati. Al fine di porre rimedio a tali problemi, il lavoro proposto in questa tesi prende in con- siderazione un particolare schema di cifratura attribute-based, chiamato inner product encryption (IPE, che gode della propriet attribute-hiding e pertanto riesce a proteggere la privatezza delle politiche di accesso) e lo combina con le tecniche di proxy re-encryption, che introducono una maggiore flessibilit ed efficienza. La prima parte di questa tesi discute l\u2019adeguatezza dell\u2019introduzione di un meccanismo di controllo degli accessi fondato su schema basato su inner product e proxy re-encryption (IPPRE ) al fine di garantire la condivisione sicura di dati immagazzinati su cloud server non fidati. Pi specificamente, proponiamo due proponiamo due versioni di IPE : in prima istanza, presentiamo una versione es- tesa con proxy re-encryption di un noto schema basato su inner product [1]. In seguito, usiamo tale schema in uno scenario in cui vengono raccolti e gestiti dati medici. In tale scenario, una volta che i dati sono stati raccolti, le politiche di ac- cesso possono variare al variare delle necessit dei diversi staff medici. Lo schema proposto delega il compito della ricifratura dei dati a un server proxy parzial- mente fidato, che pu trasformare la cifratura dei dati (che dipende da una polit- ica di accesso) in un\u2019altra cifratura (che dipende da un\u2019altra politica di accesso) senza per questo avere accesso ai dati in chiaro o alla chiave segreta utilizzata dal possessore dei dati. In tal modo, il possessore di una chiave di decifratura corrispondente alla seconda politica di accesso pu accedere ai dati senza intera- gire con il possessore dei dati (richiedendo cio una chiave di decifratura associata alla propria politica di accesso). Presentiamo un\u2019analisi relativa alle prestazioni di tale schema implementato su curve ellittiche appartenenti alle classi SS, MNT e BN e otteniamo incoraggianti risultati sperimentali. Dimostriamo inoltre che lo schema proposto sicuro contro attacchi chosen plaintext sotto la nota ipotesi DLIN. In seconda istanza, presentiamo una versione ottimizzata dello schema proposto in precedenza (E-IPPRE ), basata su un ben noto schema basato suinner product, proposto da Kim [2]. Lo schema E-IPPRE proposto richiede un numero costante di operazioni di calcolo di pairing e ci garantisce che gli oggetti prodotti dall esecuzione dello schema (chiavi di decifratura, chiavi pubbliche e le cifrature stesse) sono di piccole rispetto ai parametri di sicurezza e sono efficientemente calcolabili. Testiamo sperimentalmente l\u2019efficienza dello schema proposto e lo proviamo (selettivamente nei confronti degli attributi) sicuro nei confronti di attacchi chosen plaintext sotto la nota ipotesi BDH. In altri termini, lo schema proposto non rivela alcuna informazione riguardante le politiche di accesso. La seconda parte di questa tesi presenta uno schema crittografico per la condivisione sicura dei dati basato su crittografia attribute-based e adatto per scenari basati su IoT. Come noto, il problema principale in tale ambito riguarda le limitate risorse computazionali dei device IoT coinvolti. A tal proposito, proponiamo uno schema che combina la flessibilit di E-IPPRE con l\u2019efficienza di uno schema di cifratura simmetrico quale AES, ottenendo uno schema di cifratura basato su inner product, proxy-based leggero (L-IPPRE ). I risultati sperimentali confermano l\u2019adeguatezza di tale schema in scenari IoT.Riferimenti [1] Jong Hwan Park. Inner-product encryption under standard assumptions. Des. Codes Cryptography, 58(3):235\u2013257, March 2011. [2] Intae Kim, Seong Oun Hwang, Jong Hwan Park, and Chanil Park. An effi- cient predicate encryption with constant pairing computations and minimum costs. IEEE Trans. Comput., 65(10):2947\u20132958, October 2016.With the ever-growing production of data coming from multiple, scattered, and highly dynamical sources, many providers are motivated to upload their data to the cloud servers and share them with other persons for different purposes. However, storing data on untrusted cloud servers imposes serious concerns in terms of security, privacy, data confidentiality, and access control. In order to prevent privacy and security breaches, it is vital that data is encrypted first before it is outsourced to the cloud. However, designing access control mod- els that enable different users to have various access rights to the shared data is the main challenge. To tackle this issue, a possible solution is to employ a cryptographic-based data access control mechanism such as attribute-based encryption (ABE ) scheme, which enables a data owner to take full control over data access. However, access control mechanisms based on ABE raise two chal- lenges: (i) weak privacy: they do not conceal the attributes associated with the ciphertexts, and therefore they do not satisfy attribute-hiding security, and (ii) inefficiency: they do not support efficient access policy change when data is required to be shared among multiple users with different access policies. To address these issues, this thesis studies and enhances inner-product encryption (IPE ), a type of public-key cryptosystem, which supports the attribute-hiding property as well as the flexible fine-grained access control based payload-hiding property, and combines it with an advanced cryptographic technique known as proxy re-encryption (PRE ). The first part of this thesis discusses the necessity of applying the inner- product proxy re-encryption (IPPRE ) scheme to guarantee secure data sharing on untrusted cloud servers. More specifically, we propose two extended schemes of IPE : in the first extended scheme, we propose an inner-product proxy re- encryption (IPPRE ) protocol derived from a well-known inner-product encryp- tion scheme [1]. We deploy this technique in the healthcare scenario where data, collected by medical devices according to some access policy, has to be changed afterwards for sharing with other medical staffs. The proposed scheme delegates the re-encryption capability to a semi-trusted proxy who can transform a dele- gator\u2019s ciphertext associated with an attribute vector to a new ciphertext associ- ated with delegatee\u2019s attribute vector set, without knowing the underlying data and private key. Our proposed policy updating scheme enables the delegatee to decrypt the shared data with its own key without requesting a new decryption key. We analyze the proposed protocol in terms of its performance on three dif- ferent types of elliptic curves such as the SS curve, the MNT curve, and the BN curve, respectively. Hereby, we achieve some encouraging experimental results. We show that our scheme is adaptive attribute-secure against chosen-plaintext under standard Decisional Linear (D-Linear ) assumption. To improve the per- formance of this scheme in terms of storage, communication, and computation costs, we propose an efficient inner-product proxy re-encryption (E-IPPRE ) scheme using the transformation of Kim\u2019s inner-product encryption method [2]. The proposed E-IPPRE scheme requires constant pairing operations for its al- gorithms and ensures a short size of the public key, private key, and ciphertext,making it the most efficient and practical compared to state of the art schemes in terms of computation and communication overhead. We experimentally as- sess the efficiency of our protocol and show that it is selective attribute-secure against chosen-plaintext attacks in the standard model under Asymmetric De- cisional Bilinear Diffie-Hellman assumption. Specifically, our proposed schemes do not reveal any information about the data owner\u2019s access policy to not only the untrusted servers (e.g, cloud and proxy) but also to the other users. The second part of this thesis presents a new lightweight secure data sharing scheme based on attribute-based cryptography for a specific IoT -based health- care application. To achieve secure data sharing on IoT devices while preserving data confidentiality, the IoT devices encrypt data before it is outsourced to the cloud and authorized users, who have corresponding decryption keys, can ac- cess the data. The main challenge, in this case, is on the one hand that IoT devices are resource-constrained in terms of energy, CPU, and memory. On the other hand, the existing public-key encryption mechanisms (e.g., ABE ) require expensive computation. We address this issue by combining the flexibility and expressiveness of the proposed E-IPPRE scheme with the efficiency of symmet- ric key encryption technique (AES ) and propose a light inner-product proxy re-encryption (L-IPPRE ) scheme to guarantee secure data sharing between dif- ferent entities in the IoT environment. The experimental results confirm that the proposed L-IPPRE scheme is suitable for resource-constrained IoT scenar- ios.References [1] Jong Hwan Park. Inner-product encryption under standard assumptions. Des. Codes Cryptography, 58(3):235\u2013257, March 2011. [2] Intae Kim, Seong Oun Hwang, Jong Hwan Park, and Chanil Park. An effi- cient predicate encryption with constant pairing computations and minimum costs. IEEE Trans. Comput., 65(10):2947\u20132958, October 2016

PrivGenDB: Efficient and privacy-preserving query executions over encrypted SNP-Phenotype database

Privacy and security issues limit the query executions over genomics datasets, notably single nucleotide polymorphisms (SNPs), raised by the sensitivity of this type of data. Therefore, it is important to ensure that executing queries on these datasets do not reveal sensitive information, such as the identity of the individuals and their genetic traits, to a data server. In this paper, we propose and present a novel model, we call PrivGenDB, to ensure the confidentiality of SNP-phenotype data while executing queries. The confidentiality in PrivGenDB is enabled by its system architecture and the search functionality provided by searchable symmetric encryption (SSE). To the best of our knowledge, PrivGenDB construction is the first SSE-based approach ensuring the confidentiality of SNP-phenotype data as the current SSE-based approaches for genomic data are limited only to substring search and range queries on a sequence of genomic data. Besides, a new data encoding mechanism is proposed and incorporated in the PrivGenDB model. This enables PrivGenDB to handle the dataset containing both genotype and phenotype and also support storing and managing other metadata, like gender and ethnicity, privately. Furthermore, different queries, namely Count, Boolean, Negation and k′-out-of-k match queries used for genomic data analysis, are supported and executed by PrivGenDB. The execution of these queries on genomic data in PrivGenDB is efficient and scalable for biomedical research and services. These are demonstrated by our analytical and empirical analysis presented in this paper. Specifically, our empirical studies on a dataset with 5000 entries (records) containing 1000 SNPs demonstrate that a count/Boolean query and a k′-out-of-k match query over 40 SNPs take approximately 4.3s and 86.4μs, respectively, outperforming the existing schemes

Cryptographic Techniques for Securing Data in the Cloud

El paradigma de la computació al núvol proporciona accés remot a potents infraestructures a cost reduït. Tot i que l’adopció del núvol ofereix nombrosos beneficis, la migració de dades sol requerir un alt nivell de confiança en el proveïdor de serveis i introdueix problemes de privacitat. En aquesta tesi es dissenyen tècniques per a permetre a usuaris del núvol protegir un conjunt de dades externalitzades. Les solucions proposades emanen del projecte H2020 de la Comissió Europea “CLARUS: User-Centered Privacy and Security in the Cloud”. Els problemes explorats són la cerca sobre dades xifrades, la delegació de càlculs d’interpolació, els esquemes de compartició de secrets i la partició de dades. Primerament, s’estudia el problema de la cerca sobre dades xifrades mitjançant els esquemes de xifrat cercable simètric (SSE), i es desenvolupen tècniques que permeten consultes per rangs dos-dimensionals a SSE. També es tracta el mateix problema utilitzant esquemes de xifrat cercable de clau pública (PEKS), i es presenten esquemes PEKS que permeten consultes conjuntives i de subconjunt. En aquesta tesi també s’aborda la delegació privada de computacions Kriging. Kriging és un algoritme d’interpolació espaial dissenyat per a aplicacions geo-estadístiques. Es descriu un mètode per a delegar interpolacions Kriging de forma privada utilitzant xifrat homomòrfic. Els esquemes de compartició de secrets són una primitiva fonamental en criptografia, utilitzada a diverses solucions orientades al núvol. Una de les mesures d’eficiència relacionades més importants és la taxa d’informació òptima. Atès que calcular aquesta taxa és generalment difícil, s’obtenen propietats que faciliten la seva descripció. Finalment, es tracta el camp de la partició de dades per a la protecció de la privacitat. Aquesta tècnica protegeix la privacitat de les dades emmagatzemant diversos fragments a diferents ubicacions. Aquí s’analitza aquest problema des d’un punt de vista combinatori, fitant el nombre de fragments i proposant diversos algoritmes.El paradigma de la computación en la nube proporciona acceso remoto a potentes infraestructuras a coste reducido. Aunque la adopción de la nube ofrece numerosos beneficios, la migración de datos suele requerir un alto nivel de confianza en el proveedor de servicios e introduce problemas de privacidad. En esta tesis se diseñan técnicas para permitir a usuarios de la nube proteger un conjunto de datos externalizados. Las soluciones propuestas emanan del proyecto H2020 de la Comisión Europea “CLARUS: User-Centered Privacy and Security in the Cloud”. Los problemas explorados son la búsqueda sobre datos cifrados, la delegación de cálculos de interpolación, los esquemas de compartición de secretos y la partición de datos. Primeramente, se estudia el problema de la búsqueda sobre datos cifrados mediante los esquemas de cifrado simétrico buscable (SSE), y se desarrollan técnicas para permitir consultas por rangos dos-dimensionales en SSE. También se trata el mismo problema utilizando esquemas de cifrado buscable de llave pública (PEKS), y se presentan esquemas que permiten consultas conyuntivas y de subconjunto. Adicionalmente, se aborda la delegación privada de computaciones Kriging. Kriging es un algoritmo de interpolación espacial diseñado para aplicaciones geo-estadísticas. Se describe un método para delegar interpolaciones Kriging privadamente utilizando técnicas de cifrado homomórfico. Los esquemas de compartición de secretos son una primitiva fundamental en criptografía, utilizada en varias soluciones orientadas a la nube. Una de las medidas de eficiencia más importantes es la tasa de información óptima. Dado que calcular esta tasa es generalmente difícil, se obtienen propiedades que facilitan su descripción. Por último, se trata el campo de la partición de datos para la protección de la privacidad. Esta técnica protege la privacidad de los datos almacenando varios fragmentos en distintas ubicaciones. Analizamos este problema desde un punto de vista combinatorio, acotando el número de fragmentos y proponiendo varios algoritmos.The cloud computing paradigm provides users with remote access to scalable and powerful infrastructures at a very low cost. While the adoption of cloud computing yields a wide array of benefits, the act of migrating to the cloud usually requires a high level of trust in the cloud service provider and introduces several security and privacy concerns. This thesis aims at designing user-centered techniques to secure an outsourced data set in cloud computing. The proposed solutions stem from the European Commission H2020 project “CLARUS: User-Centered Privacy and Security in the Cloud”. The explored problems are searching over encrypted data, outsourcing Kriging interpolation computations, secret sharing and data splitting. Firstly, the problem of searching over encrypted data is studied using symmetric searchable encryption (SSE) schemes, and techniques are developed to enable efficient two-dimensional range queries in SSE. This problem is also studied through public key encryption with keyword search (PEKS) schemes, efficient PEKS schemes achieving conjunctive and subset queries are proposed. This thesis also aims at securely outsourcing Kriging computations. Kriging is a spatial interpolation algorithm designed for geo-statistical applications. A method to privately outsource Kriging interpolation is presented, based in homomorphic encryption. Secret sharing is a fundamental primitive in cryptography, used in many cloud-oriented techniques. One of the most important efficiency measures in secret sharing is the optimal information ratio. Since computing the optimal information ratio of an access structure is generally hard, properties are obtained to facilitate its description. Finally, this thesis tackles the privacy-preserving data splitting technique, which aims at protecting data privacy by storing different fragments of data at different locations. Here, the data splitting problem is analyzed from a combinatorial point of view, bounding the number of fragments and proposing various algorithms to split the data

Private set intersection: A systematic literature review

Secure Multi-party Computation (SMPC) is a family of protocols which allow some parties to compute a function on their private inputs, obtaining the output at the end and nothing more. In this work, we focus on a particular SMPC problem named Private Set Intersection (PSI). The challenge in PSI is how two or more parties can compute the intersection of their private input sets, while the elements that are not in the intersection remain private. This problem has attracted the attention of many researchers because of its wide variety of applications, contributing to the proliferation of many different approaches. Despite that, current PSI protocols still require heavy cryptographic assumptions that may be unrealistic in some scenarios. In this paper, we perform a Systematic Literature Review of PSI solutions, with the objective of analyzing the main scenarios where PSI has been studied and giving the reader a general taxonomy of the problem together with a general understanding of the most common tools used to solve it. We also analyze the performance using different metrics, trying to determine if PSI is mature enough to be used in realistic scenarios, identifying the pros and cons of each protocol and the remaining open problems.This work has been partially supported by the projects: BIGPrivDATA (UMA20-FEDERJA-082) from the FEDER Andalucía 2014– 2020 Program and SecTwin 5.0 funded by the Ministry of Science and Innovation, Spain, and the European Union (Next Generation EU) (TED2021-129830B-I00). The first author has been funded by the Spanish Ministry of Education under the National F.P.U. Program (FPU19/01118). Funding for open access charge: Universidad de Málaga/CBU

Data security in cloud storage services

Cloud Computing is considered to be the next-generation architecture for ICT where it moves the application software and databases to the centralized large data centers. It aims to offer elastic IT services where clients can benefit from significant cost savings of the pay-per-use model and can easily scale up or down, and do not have to make large investments in new hardware. However, the management of the data and services in this cloud model is under the control of the provider. Consequently, the cloud clients have less control over their outsourced data and they have to trust cloud service provider to protect their data and infrastructure from both external and internal attacks. This is especially true with cloud storage services. Nowadays, users rely on cloud storage as it offers cheap and unlimited data storage that is available for use by multiple devices (e.g. smart phones, tablets, notebooks, etc.). Besides famous cloud storage providers, such as Amazon, Google, and Microsoft, more and more third-party cloud storage service providers are emerging. These services are dedicated to offering more accessible and user friendly storage services to cloud customers. Examples of these services include Dropbox, Box.net, Sparkleshare, UbuntuOne or JungleDisk. These cloud storage services deliver a very simple interface on top of the cloud storage provided by storage service providers. File and folder synchronization between different machines, sharing files and folders with other users, file versioning as well as automated backups are the key functionalities of these emerging cloud storage services. Cloud storage services have changed the way users manage and interact with data outsourced to public providers. With these services, multiple subscribers can collaboratively work and share data without concerns about their data consistency, availability and reliability. Although these cloud storage services offer attractive features, many customers have not adopted these services. Since data stored in these services is under the control of service providers resulting in confidentiality and security concerns and risks. Therefore, using cloud storage services for storing valuable data depends mainly on whether the service provider can offer sufficient security and assurance to meet client requirements. From the way most cloud storage services are constructed, we can notice that these storage services do not provide users with sufficient levels of security leading to an inherent risk on users\u27 data from external and internal attacks. These attacks take the form of: data exposure (lack of data confidentiality); data tampering (lack of data integrity); and denial of data (lack of data availability) by third parties on the cloud or by the cloud provider himself. Therefore, the cloud storage services should ensure the data confidentiality in the following state: data in motion (while transmitting over networks), data at rest (when stored at provider\u27s disks). To address the above concerns, confidentiality and access controllability of outsourced data with strong cryptographic guarantee should be maintained. To ensure data confidentiality in public cloud storage services, data should be encrypted data before it is outsourced to these services. Although, users can rely on client side cloud storage services or software encryption tools for encrypting user\u27s data; however, many of these services fail to achieve data confidentiality. Box, for example, does not encrypt user files via SSL and within Box servers. Client side cloud storage services can intentionally/unintentionally disclose user decryption keys to its provider. In addition, some cloud storage services support convergent encryption for encrypting users\u27 data exposing it to “confirmation of a file attack. On the other hand, software encryption tools use full-disk encryption (FDE) which is not feasible for cloud-based file sharing services, because it encrypts the data as virtual hard disks. Although encryption can ensure data confidentiality; however, it fails to achieve fine-grained access control over outsourced data. Since, public cloud storage services are managed by un-trusted cloud service provider, secure and efficient fine-grained access control cannot be realized through these services as these policies are managed by storage services that have full control over the sharing process. Therefore, there is not any guarantee that they will provide good means for efficient and secure sharing and they can also deduce confidential information about the outsourced data and users\u27 personal information. In this work, we would like to improve the currently employed security measures for securing data in cloud store services. To achieve better data confidentiality for data stored in the cloud without relying on cloud service providers (CSPs) or putting any burden on users, in this thesis, we designed a secure cloud storage system framework that simultaneously achieves data confidentiality, fine-grained access control on encrypted data and scalable user revocation. This framework is built on a third part trusted (TTP) service that can be employed either locally on users\u27 machine or premises, or remotely on top of cloud storage services. This service shall encrypts users data before uploading it to the cloud and decrypts it after downloading from the cloud; therefore, it remove the burden of storing, managing and maintaining encryption/decryption keys from data owner\u27s. In addition, this service only retains user\u27s secret key(s) not data. Moreover, to ensure high security for these keys, it stores them on hardware device. Furthermore, this service combines multi-authority ciphertext policy attribute-based encryption (CP-ABE) and attribute-based Signature (ABS) for achieving many-read-many-write fine-grained data access control on storage services. Moreover, it efficiently revokes users\u27 privileges without relying on the data owner for re-encrypting massive amounts of data and re-distributing the new keys to the authorized users. It removes the heavy computation of re-encryption from users and delegates this task to the cloud service provider (CSP) proxy servers. These proxy servers achieve flexible and efficient re-encryption without revealing underlying data to the cloud. In our designed architecture, we addressed the problem of ensuring data confidentiality against cloud and against accesses beyond authorized rights. To resolve these issues, we designed a trusted third party (TTP) service that is in charge of storing data in an encrypted format in the cloud. To improve the efficiency of the designed architecture, the service allows the users to choose the level of severity of the data and according to this level different encryption algorithms are employed. To achieve many-read-many-write fine grained access control, we merge two algorithms (multi-authority ciphertext policy attribute-based encryption (MA- CP-ABE) and attribute-based Signature (ABS)). Moreover, we support two levels of revocation: user and attribute revocation so that we can comply with the collaborative environment. Last but not least, we validate the effectiveness of our design by carrying out a detailed security analysis. This analysis shall prove the correctness of our design in terms of data confidentiality each stage of user interaction with the cloud

Crowdsourcing atop blockchains

Traditional crowdsourcing systems, such as Amazon\u27s Mechanical Turk (MTurk), though once acquiring great economic successes, have to fully rely on third-party platforms to serve between the requesters and the workers for basic utilities. These third-parties have to be fully trusted to assist payments, resolve disputes, protect data privacy, manage user authentications, maintain service online, etc. Nevertheless, tremendous real-world incidents indicate how elusive it is to completely trust these platforms in reality, and the reduction of such over-reliance becomes desirable. In contrast to the arguably vulnerable centralized approaches, a public blockchain is a distributed and transparent global consensus computer that is highly robust. The blockchain is usually managed and replicated by a large-scale peer-to-peer network collectively, thus being much more robust to be fully trusted for correctness and availability. It, therefore, becomes enticing to build novel crowdsourcing applications atop blockchains to reduce the over-trust on third-party platforms. However, this new fascinating technology also brings about new challenges, which were never that severe in the conventional centralized setting. The most serious issue is that the blockchain is usually maintained in the public Internet environment with a broader attack surface open to anyone. This not only causes serious privacy and security issues, but also allows the adversaries to exploit the attack surface to hamper more basic utilities. Worse still, most existing blockchains support only light on-chain computations, and the smart contract executed atop the decentralized consensus computer must be simple, which incurs serious feasibility problems. In reality, the privacy/security issue and the feasibility problem even restrain each other and create serious tensions to hinder the broader adoption of blockchain. The dissertation goes through the non-trivial challenges to realize secure yet still practical decentralization (for urgent crowdsourcing use-cases), and lay down the foundation for this line of research. In sum, it makes the next major contributions. First, it identifies the needed security requirements in decentralized knowledge crowdsourcing (e.g., data privacy), and initiates the research of private decentralized crowdsourcing. In particular, the confidentiality of solicited data is indispensable to prevent free-riders from pirating the others\u27 submissions, thus ensuring the quality of solicited knowledge. To this end, a generic private decentralized crowdsourcing framework is dedicatedly designed, analyzed, and implemented. Furthermore, this dissertation leverages concretely efficient cryptographic design to reduce the cost of the above generic framework. It focuses on decentralizing the special use-case of Amazon MTurk, and conducts multiple specific-purpose optimizations to remove needless generality to squeeze performance. The implementation atop Ethereum demonstrates a handling cost even lower than MTurk. In addition, it focuses on decentralized crowdsourcing of computing power for specific machine learning tasks. It lets a requester place deposits in the blockchain to recruit some workers for a designated (randomized) programs. If and only if these workers contribute their resources to compute correctly, they would earn well-deserved payments. For these goals, a simple yet still useful incentive mechanism is developed atop the blockchain to deter rational workers from cheating. Finally, the research initiates the first systematic study on crowdsourcing blockchains\u27 full nodes to assist superlight clients (e.g., mobile phones and IoT devices) to read the blockchain\u27s records. This dissertation presents a novel generic solution through the powerful lens of game-theoretic treatments, which solves the long-standing open problem of designing generic superlight clients for all blockchains

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read