Secure Multi-party Computation Protocols from a High-Level Programming Language

Turvalise ühisarvutuse abil on võimalik sooritada privaatsust säilitavaid arvutusi mitmelt osapoolelt kogutud andmetega. Tänapäeva digitaalses maailmas on andmete konfidentsiaalsuse tagamine üha raskemini teostatav. Turvalise ühisarvutuse meetodid nagu ühissalastus ja Yao sogastatud loogikaskeemid võimaldavad teostada privaatsust säilitavaid arvutusprotokolle, mis ei lekita konfidentsiaalseid sisendandmeid. Aditiivne ühissalastuse skeem on väga efektiivne algebraliste ringide tehete sooritamiseks fikseeritud bitilaiusega andmetüüpide peal. Samas on seda kasutades raske ehitada protokolle, mis nõuavad paindlikumaid bititaseme operatsioone. Yao sogastatud loogikaskeemide meetod töötab aga igasuguse bitilaiusega andmete peal ja võimaldab väärtustada mistahes Boole'i funktsioone. Neid kahte meetodit koos kasutades ehitame turvalise hübriidprotokolli, mis kujutab endast üldist meetodit privaatsust säilitavate arvutuste teostamiseks bitikaupa ühissalastatud andmete peal. Loogikaskeeme vajalikeks arvutusteks on lihtne saada kahe kaasaegse turvalise ühisarvutuse jaoks mõeldud kompilaatori abil, mis muundavad C programmi loogikaskeemiks --- PCF ja CBMC-GC. Meie hübriidprotokolli prototüüp privaatsust säilitaval arvutusplatvormil Sharemind saavutab praktilisi jõudlustulemusi, mis on võrreldavad teiste kaasaegsete lahendustega. Lisaks kahe osapoolega arvutustele pakub meie prototüüp võimekust teostada mitmekesiseid arvutusi üldises turvalise ühisarvutuse arvutusmudelis. Hübriidprotokoll ja loogikaskeemide kompilaatorid võimaldavad koos kasutades lihtsalt ja efektiivselt luua üldkasutatavaid turvalise ühisarvutuse protokolle mistahes Boole'i funktsioonide väärtustamiseks.Secure multi-party computation (SMC) enables privacy-preserving computations on data originating from a number of parties. In today's digital world, data privacy is increasingly more difficult to provide. With SMC methods like secret sharing and Yao's garbled circuits, it is possible to build privacy-preserving computational protocols that do not leak confidential inputs to other parties. The additive secret sharing scheme is very efficient for algebraic ring operations on fixed bit-length data types. However, it is difficult to build protocols that require robust bit-level manipulation. Yao's garbled circuits approach, in contrast, works on arbitrary bit-length data and allows the evaluation of any Boolean function. Combining the two methods, we build a secure hybrid protocol, which provides a general method for building arbitrary secure computations on bitwise secret-shared data. We are able to generate circuits for the protocol easily by using two state-of-the-art C to circuit compilers designed for SMC applications --- PCF and CBMC-GC. Our hybrid protocol prototype on the Sharemind privacy-preserving computational platform achieves practical performance comparable to other recent work. In addition to two-party computations, our prototype provides the ability to perform a set of diverse computations in a generic SMC computational model. The hybrid protocol together with the circuit compilers provides a simple and efficient toolchain to build general-purpose SMC protocols for evaluating any Boolean function

Oblivious Sensor Fusion via Secure Multi-Party Combinatorial Filter Evaluation

This thesis examines the problem of fusing data from several sensors, potentially distributed throughout an environment, in order to consolidate readings into a single coherent view. We consider the setting when sensor units do not wish others to know their specific sensor streams. Standard methods for handling this fusion make no guarantees about what a curious observer may learn. Motivated by applications where data sources may only choose to participate if given privacy guarantees, we introduce a fusion approach that limits what can be inferred. Our approach is to form an aggregate stream, oblivious to the underlying sensor data, and to evaluate a combinatorial filter on that stream. This is achieved via secure multi-party computational techniques built on cryptographic primitives, which we extend and apply to the problem of fusing discrete sensor signals. We prove that the extensions preserve security under the semi- honest adversary model. Though the approach enables several applications of potential interest, we specifically consider a target tracking case study as a running example. Finally, we also report on a basic, proof-of-concept implementation, demonstrating that it can operate in practice; which we report and analyze the (empirical) running times for components in the architecture, suggesting directions for future improvement

Double Encryption Based Auditing Protocol Using Dynamic Operation in Cloud Storage

Using Cloud Storage, users can tenuously store their data and enjoy the on-demand great quality applications and facilities from a shared pool of configurable computing resources, without the problem of local data storage and maintenance. However, the fact that users no longer have physical possession of the outsourced data makes the data integrity protection in Cloud Computing a formidable task, especially for users with constrained dividing resources. From users? perspective, including both individuals and IT systems, storing data remotely into the cloud in a flexible on-demand manner brings tempting benefits: relief of the burden for storage management, universal data access with independent geographical locations, and avoidance of capital expenditure on hardware, software, and personnel maintenances, etc. . To securely introduce an effective third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to capably audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should take in no new vulnerabilities towards user data privacy. In this project, utilize and uniquely combine the public auditing protocols with double encryption approach to achieve the privacy-preserving public cloud data auditing system, which meets all integrity checking without any leakage of data. To support efficient handling of multiple auditing tasks, we further explore the technique of online signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. We can implement double encryption algorithm encrypt the data twice and stored cloud server

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor