Rmind: a tool for cryptographically secure statistical analysis

Secure multi-party computation platforms are becoming more and more practical. This has paved the way for privacy-preserving statistical analysis using secure multi-party computation. Simple statistical analysis functions have been emerging here and there in literature, but no comprehensive system has been compiled. We describe and implement the most used statistical analysis functions in the privacy-preserving setting including simple statistics, t-test, $\chi^{2}$ test, Wilcoxon tests and linear regression. We give descriptions of the privacy-preserving algorithms and benchmark results that show the feasibility of our solution

CryptoMaze: Atomic Off-Chain Payments in Payment Channel Network

Payment protocols developed to realize off-chain transactions in Payment channel network (PCN) assumes the underlying routing algorithm transfers the payment via a single path. However, a path may not have sufficient capacity to route a transaction. It is inevitable to split the payment across multiple paths. If we run independent instances of the protocol on each path, the execution may fail in some of the paths, leading to partial transfer of funds. A payer has to reattempt the entire process for the residual amount. We propose a secure and privacy-preserving payment protocol, CryptoMaze. Instead of independent paths, the funds are transferred from sender to receiver across several payment channels responsible for routing, in a breadth-first fashion. Payments are resolved faster at reduced setup cost, compared to existing state-of-the-art. Correlation among the partial payments is captured, guaranteeing atomicity. Further, two party ECDSA signature is used for establishing scriptless locks among parties involved in the payment. It reduces space overhead by leveraging on core Bitcoin scripts. We provide a formal model in the Universal Composability framework and state the privacy goals achieved by CryptoMaze. We compare the performance of our protocol with the existing single path based payment protocol, Multi-hop HTLC, applied iteratively on one path at a time on several instances. It is observed that CryptoMaze requires less communication overhead and low execution time, demonstrating efficiency and scalability.Comment: 30 pages, 9 figures, 1 tabl

Processing Over Encrypted Query Data In Internet of Things (IoTs) : CryptDBs, MONOMI and SDB

Internet of Things (IoT) is the developing technologies that would be the biggest agents to modify the current world. Machine-to-machine communications perform with virtual, mobile and instantaneous connections. In IoT system, it consists of data-gathering sensors various other household devices. Intended for protecting IoT system, the end-to-end secure communication is a necessary measure to protect against unauthorized entities (e.g., modification attacks and eavesdropping,) and the data unprotected on the Cloud. The most important concern hereby is how to preserve the insightful information and to provide the privacy of user data. In IoT, the encrypted data computing is based on techniques appear to be promising approaches. In this paper, we discuss about the recent secure database systems, which are capable to execute SQL queries over encrypted data

Privacy-Preserving Health Data Collection for Preschool Children

With the development of network technology, more and more data are transmitted over the network and privacy issues have become a research focus. In this paper, we study the privacy in health data collection of preschool children and present a new identity-based encryption protocol for privacy protection. The background of the protocol is as follows. A physical examination for preschool children is needed every year out of consideration for the children's health. After the examination, data are transmitted through the Internet to the education authorities for analysis. In the process of data collection, it is unnecessary for the education authorities to know the identities of the children. Based on this, we designed a privacy-preserving protocol, which delinks the children’s identities from the examination data. Thus, the privacy of the children is preserved during data collection. We present the protocol in detail and prove the correctness of the protocol

Turvalisel ühisarvutusel põhinev privaatsust säilitav statistiline analüüs

Väitekirja elektrooniline versioon ei sisalda publikatsioone.Kaasaegses ühiskonnas luuakse inimese kohta digitaalne kirje kohe pärast tema sündi. Sellest hetkest alates jälgitakse tema käitumist ning kogutakse andmeid erinevate eluvaldkondade kohta. Kui kasutate poes kliendikaarti, käite arsti juures, täidate maksudeklaratsiooni või liigute lihtsalt ringi mobiiltelefoni taskus kandes, koguvad ning salvestavad firmad ja riigiasutused teie tundlikke andmeid. Vahel anname selliseks jälitustegevuseks vabatahtlikult loa, et saada mingit kasu. Näiteks võime saada soodustust, kui kasutame kliendikaarti. Teinekord on meil vaja teha keeruline otsus, kas loobuda võimalusest teha mobiiltelefonikõnesid või lubada enda jälgimine mobiilimastide kaudu edastatava info abil. Riigiasutused haldavad infot meie tervise, hariduse ja sissetulekute kohta, et meid paremini ravida, harida ja meilt makse koguda. Me loodame, et meie andmeid kasutatakse mõistlikult, aga samas eeldame, et meie privaatsus on tagatud. Käesolev töö uurib, kuidas teostada statistilist analüüsi nii, et tagada üksikisiku privaatsus. Selle eesmärgi saavutamiseks kasutame turvalist ühisarvutust. See krüptograafiline meetod lubab analüüsida andmeid nii, et üksikuid väärtuseid ei ole kunagi võimalik näha. Hoolimata sellest, et turvalise ühisarvutuse kasutamine on aeganõudev protsess, näitame, et see on piisavalt kiire ja seda on võimalik kasutada isegi väga suurte andmemahtude puhul. Me oleme teinud võimalikuks populaarseimate statistilise analüüsi meetodite kasutamise turvalise ühisarvutuse kontekstis. Me tutvustame privaatsust säilitavat statistilise analüüsi tööriista Rmind, mis sisaldab kõiki töö käigus loodud funktsioone. Rmind sarnaneb tööriistadele, millega statistikud on harjunud. See lubab neil viia läbi uuringuid ilma, et nad peaksid üksikasjalikult tundma allolevaid krüptograafilisi protokolle. Kasutame dissertatsioonis kirjeldatud meetodeid, et valmistada ette statistiline uuring, mis ühendab kaht Eesti riiklikku andmekogu. Uuringu eesmärk on teada saada, kas Eesti tudengid, kes töötavad ülikooliõpingute ajal, lõpetavad nominaalajaga väiksema tõenäosusega kui nende õpingutele keskenduvad kaaslased.In a modern society, from the moment a person is born, a digital record is created. From there on, the person’s behaviour is constantly tracked and data are collected about the different aspects of his or her life. Whether one is swiping a customer loyalty card in a store, going to the doctor, doing taxes or simply moving around with a mobile phone in one’s pocket, sensitive data are being gathered and stored by governments and companies. Sometimes, we give our permission for this kind of surveillance for some benefit. For instance, we could get a discount using a customer loyalty card. Other times we have a difficult choice – either we cannot make phone calls or our movements are tracked based on cellular data. The government tracks information about our health, education and income to cure us, educate us and collect taxes. We hope that the data are used in a meaningful way, however, we also have an expectation of privacy. This work focuses on how to perform statistical analyses in a way that preserves the privacy of the individual. To achieve this goal, we use secure multi-­‐party computation. This cryptographic technique allows data to be analysed without seeing the individual values. Even though using secure multi-­‐party computation is a time-­‐consuming process, we show that it is feasible even for large-­‐scale databases. We have developed ways for using the most popular statistical analysis methods with secure multi-­‐party computation. We introduce a privacy-­‐preserving statistical analysis tool called Rmind that contains all of our resulting implementations. Rmind is similar to tools that statistical analysts are used to. This allows them to carry out studies on the data without having to know the details of the underlying cryptographic protocols. The methods described in the thesis are used in practice to prepare for running a statistical study on large-­‐scale real-­‐life data to find out whether Estonian students who are working during university studies are less likely to graduate in nominal time

Security and blockchain convergence with internet of multimedia things : current trends, research challenges and future directions

The Internet of Multimedia Things (IoMT) orchestration enables the integration of systems, software, cloud, and smart sensors into a single platform. The IoMT deals with scalar as well as multimedia data. In these networks, sensor-embedded devices and their data face numerous challenges when it comes to security. In this paper, a comprehensive review of the existing literature for IoMT is presented in the context of security and blockchain. The latest literature on all three aspects of security, i.e., authentication, privacy, and trust is provided to explore the challenges experienced by multimedia data. The convergence of blockchain and IoMT along with multimedia-enabled blockchain platforms are discussed for emerging applications. To highlight the significance of this survey, large-scale commercial projects focused on security and blockchain for multimedia applications are reviewed. The shortcomings of these projects are explored and suggestions for further improvement are provided. Based on the aforementioned discussion, we present our own case study for healthcare industry: a theoretical framework having security and blockchain as key enablers. The case study reflects the importance of security and blockchain in multimedia applications of healthcare sector. Finally, we discuss the convergence of emerging technologies with security, blockchain and IoMT to visualize the future of tomorrow's applications. © 2020 Elsevier Lt

Applying Secure Multi-party Computation in Practice

In this work, we present solutions for technical difficulties in deploying secure multi-party computation in real-world applications. We will first give a brief overview of the current state of the art, bring out several shortcomings and address them. The main contribution of this work is an end-to-end process description of deploying secure multi-party computation for the first large-scale registry-based statistical study on linked databases. Involving large stakeholders like government institutions introduces also some non-technical requirements like signing contracts and negotiating with the Data Protection Agency

DECO: Liberating Web Data Using Decentralized Oracles for TLS

Thanks to the widespread deployment of TLS, users can access private data over channels with end-to-end confidentiality and integrity. What they cannot do, however, is prove to third parties the {\em provenance} of such data, i.e., that it genuinely came from a particular website. Existing approaches either introduce undesirable trust assumptions or require server-side modifications. As a result, the value of users' private data is locked up in its point of origin. Users cannot export their data with preserved integrity to other applications without help and permission from the current data holder. We propose DECO (short for \underline{dec}entralized \underline{o}racle) to address the above problems. DECO allows users to prove that a piece of data accessed via TLS came from a particular website and optionally prove statements about such data in zero-knowledge, keeping the data itself secret. DECO is the first such system that works without trusted hardware or server-side modifications. DECO can liberate data from centralized web-service silos, making it accessible to a rich spectrum of applications. To demonstrate the power of DECO, we implement three applications that are hard to achieve without it: a private financial instrument using smart contracts, converting legacy credentials to anonymous credentials, and verifiable claims against price discrimination.Comment: This is the extended version of the CCS'20 pape

Towards end-to-end security in internet of things based healthcare

Healthcare IoT systems are distinguished in that they are designed to serve human beings, which primarily raises the requirements of security, privacy, and reliability. Such systems have to provide real-time notifications and responses concerning the status of patients. Physicians, patients, and other caregivers demand a reliable system in which the results are accurate and timely, and the service is reliable and secure. To guarantee these requirements, the smart components in the system require a secure and efficient end-to-end communication method between the end-points (e.g., patients, caregivers, and medical sensors) of a healthcare IoT system. The main challenge faced by the existing security solutions is a lack of secure end-to-end communication. This thesis addresses this challenge by presenting a novel end-to-end security solution enabling end-points to securely and efficiently communicate with each other. The proposed solution meets the security requirements of a wide range of healthcare IoT systems while minimizing the overall hardware overhead of end-to-end communication. End-to-end communication is enabled by the holistic integration of the following contributions. The first contribution is the implementation of two architectures for remote monitoring of bio-signals. The first architecture is based on a low power IEEE 802.15.4 protocol known as ZigBee. It consists of a set of sensor nodes to read data from various medical sensors, process the data, and send them wirelessly over ZigBee to a server node. The second architecture implements on an IP-based wireless sensor network, using IEEE 802.11 Wireless Local Area Network (WLAN). The system consists of a IEEE 802.11 based sensor module to access bio-signals from patients and send them over to a remote server. In both architectures, the server node collects the health data from several client nodes and updates a remote database. The remote webserver accesses the database and updates the webpage in real-time, which can be accessed remotely. The second contribution is a novel secure mutual authentication scheme for Radio Frequency Identification (RFID) implant systems. The proposed scheme relies on the elliptic curve cryptography and the D-Quark lightweight hash design. The scheme consists of three main phases: (1) reader authentication and verification, (2) tag identification, and (3) tag verification. We show that among the existing public-key crypto-systems, elliptic curve is the optimal choice due to its small key size as well as its efficiency in computations. The D-Quark lightweight hash design has been tailored for resource-constrained devices. The third contribution is proposing a low-latency and secure cryptographic keys generation approach based on Electrocardiogram (ECG) features. This is performed by taking advantage of the uniqueness and randomness properties of ECG's main features comprising of PR, RR, PP, QT, and ST intervals. This approach achieves low latency due to its reliance on reference-free ECG's main features that can be acquired in a short time. The approach is called Several ECG Features (SEF)-based cryptographic key generation. The fourth contribution is devising a novel secure and efficient end-to-end security scheme for mobility enabled healthcare IoT. The proposed scheme consists of: (1) a secure and efficient end-user authentication and authorization architecture based on the certificate based Datagram Transport Layer Security (DTLS) handshake protocol, (2) a secure end-to-end communication method based on DTLS session resumption, and (3) support for robust mobility based on interconnected smart gateways in the fog layer. Finally, the fifth and the last contribution is the analysis of the performance of the state-of-the-art end-to-end security solutions in healthcare IoT systems including our end-to-end security solution. In this regard, we first identify and present the essential requirements of robust security solutions for healthcare IoT systems. We then analyze the performance of the state-of-the-art end-to-end security solutions (including our scheme) by developing a prototype healthcare IoT system