Biblio

Transmitting data through the internet may have severe security risks due to illegal access done by attackers. Some methods have been introduced to overcome this issue, such as cryptography and steganography. Nevertheless, some problems still arise, such as the quality of the stego data. Specifically, it happens if the stego is shared with some users. In this research, a shared-secret mechanism is combined with steganography. For this purpose, the fuzzy logic edge detection and Prediction Error (PE) methods are utilized to hide private data. The secret sharing process is carried out after data embedding in the cover image. This sharing mechanism is performed on image pixels that have been converted to PE values. Various Peak Signal to Noise Ratio (PSNR) values are obtained from the experiment. It is found that the number of participants and the threshold do not significantly affect the image quality of the shares.

Ensuring sustainable sourcing of crude materials and production of goods is a pressing problem in consideration of the growing world population and rapid climate change. Supply-chain traceability systems based on distributed ledgers can help to enforce sustainability policies like production limits. We propose two mutually independent distributed-ledger-based protocols that enable public verifiability of policy compliance. They are designed for different supply-chain scenarios and use different privacy-enhancing technologies in order to protect confidential supply-chain data: secret sharing and homomorphic encryption. The protocols can be added to existing supply-chain traceability solutions with minor effort. They ensure confidentiality of transaction details and offer public verifiability of producers' compliance, enabling institutions and even end consumers to evaluate sustainability of supply chains. Through extensive theoretical and empirical evaluation, we show that both protocols perform verification for lifelike supply-chain scenarios in perfectly practical time.

In order to solve the problem of difficult verification of query results in searchable encryption, we used the idea of Shamir-secret sharing, combined with game theory, to construct a randomly verifiable multi-cloud server searchable encryption scheme to achieve the correctness of the query results in the cloud storage environment verify. Firstly, we using the Shamir-secret sharing technology, the encrypted data is stored on each independent server to construct a multi-cloud server model to realize the secure distributed storage and efficient query of data. Secondly, combined with game theory, a game tree of query server and verification server is constructed to ensure honesty while being efficient, and solve the problem of difficulty in returning search results to verify under the multi-cloud server model. Finally, security analysis and experimental analysis show that this solution effectively protects data privacy while significantly reducing retrieval time.

Information-centric networking (ICN) has played an increasingly important role in the next generation network design. However, to make better use of request-response communication mode in the ICN network, revoke user privileges more efficiently and protect user privacy more safely, an effective access control mechanism is needed. In this paper, we propose IBSS (identity-based secret sharing), which achieves efficient content distribution by using improved Shamir's secret sharing method. At the same time, collusion attacks are avoided by associating polynomials' degree with the number of users. When authenticating user identity and transmitting content, IBE and IBS are introduced to achieve more efficient and secure identity encryption. From the experimental results, the scheme only introduces an acceptable delay in file retrieval, and it can request follow-up content very efficiently.

Data can be stored securely in various storage servers. But in the case of a server failure, or data theft from a certain number of servers, the remaining data becomes inadequate for use. Data is stored securely using secret sharing schemes, so that data can be reconstructed even if some of the servers fail. But not much work has been carried out in the direction of updation of this data. This leads to the problem of updation when two or more concurrent requests arrive and thus, it results in inconsistency. Our work proposes a novel method to store data securely with concurrent update requests using Petri Nets, under the assumption that the number of nodes is very large and the requests for updates are very frequent.

In this paper, we propose a conditional privacy-preserving authentication scheme based on pseudonyms and (t,n) threshold secret sharing, named CPPT, for vehicular communications. To achieve conditional privacy preservation, our scheme implements anonymous communications based on pseudonyms generated by hash chains. To prevent bad vehicles from conducting framed attacks on honest ones, CPPT introduces Shamir (t,n) threshold secret sharing technique. In addition, through two one-way hash chains, forward security and backward security are guaranteed, and it also optimize the revocation overhead. The size of certificate revocation list (CRL) is only proportional to the number of revoked vehicles and irrelated to how many pseudonymous certificates are held by the revoked vehicles. Extensive simulations demonstrate that CPPT outperforms ECPP, DCS, Hybrid and EMAP schemes in terms of revocation overhead, certificate updating overhead and authentication overhead.

SM2 is an elliptic curve public key cryptography algorithm released by the State Cryptography Administration of China. It includes digital signature, data encryption and key exchange schemes. To meet specific application requirements, such as to protect the user's private key in software only implementation, and to facilitate secure cloud cryptography computing, secret sharing based SM2 signature generation schemes have been proposed in the literature. In this paper a new such kind of scheme based upon additively homomorphic encryption is proposed. The proposed scheme overcomes the drawback that the existing schemes have and is more secure. It is useful in various application scenarios.

Tensor operations, such as matrix multiplication, are central to large-scale machine learning applications. These operations can be carried out on a distributed computing platform with a master server at the user side and multiple workers in the cloud operating in parallel. For distributed platforms, it has been recently shown that coding over the input data matrices can reduce the computational delay, yielding a tradeoff between recovery threshold and communication load. In this work, we impose an additional security constraint on the data matrices and assume that workers can collude to eavesdrop on the content of these data matrices. Specifically, we introduce a novel class of secure codes, referred to as secure generalized PolyDot codes, that generalizes previously published non-secure versions of these codes for matrix multiplication. These codes extend the state-of-the-art by allowing a flexible trade-off between recovery threshold and communication load for a fixed maximum number of colluding workers.

The main searching functions realized by searchable encryption can be divided into searching using one query and searching using multiple queries. Searchable encryption using one query has been widely studied and researched; however, few methods of searchable encryption can accommodate search using multiple queries. In addition, most of the method proposed thus far utilize the concept of index search. Therefore, a new problem exists, in which an additional process of updating or deleting an index when new documents are added or removed is required. Hence, the overall computation cost increases. Another problem is that a document that is not registered in the index cannot be searched. Therefore, herein, using a secret-sharing scheme that is known to offer a low computational cost, we propose a method that can realize both logical conjunctive (AND) and logical disjunctive (OR) search over multiple conditions, without the construction of any index. Hence, we can realize direct searching over sentences, thus achieving a more efficient search method.

Today's extensive use of Internet creates huge volumes of data by users in both client and server sides. Normally users don't want to store all the data in local as well as keep archive in the server. For some unwanted data, such as trash, cache and private data, needs to be deleted periodically. Explicit deletion could be applied to the local data, while it is a troublesome job. But there is no transparency to users on the personal data stored in the server. Since we have no knowledge of whether they're cached, copied and archived by the third parties, or sold by the service provider. Our research seeks to provide an automatic data sanitization system to make data could be self-destructing. Specifically, we give data a life cycle, which would be erased automatically when at the end of its life, and the destroyed data cannot be recovered by any effort. In this paper, we present FlashGhost, which is a system that meets this challenge through a novel integration of cryptography techniques with the frequent colliding hash table. In this system, data will be unreadable and rendered unrecoverable by overwriting multiple times after its validity period has expired. Besides, the system reliability is enhanced by threshold cryptography. We also present a mathematical model and verify it by a number of experiments, which demonstrate theoretically and experimentally our system is practical to use and meet the data auto-sanitization goal described above.

The problem of Byzantine Agreement (BA) is of interest to both distributed computing and cryptography community. Following well-known results from the distributed computing literature, BA problem in the asynchronous network setting encounters inevitable non-termination issues. The impasse is overcome via randomization that allows construction of BA protocols in two flavours of termination guarantee - with overwhelming probability and with probability one. The latter type termed as almost-surely terminating BAs are the focus of this paper. An eluding problem in the domain of almost-surely terminating BAs is achieving a constant expected running time. Our work makes progress in this direction. In a setting with n parties and an adversary with unbounded computing power controlling at most t parties in Byzantine fashion, we present two asynchronous almost-surely terminating BA protocols: With the optimal resilience of t \textbackslashtextless n3 , our first protocol runs for expected O(n) time. The existing protocols in the same setting either runs for expected O(n2) time (Abraham et al, PODC 2008) or requires exponential computing power from the honest parties (Wang, CoRR 2015). In terms of communication complexity, our construction outperforms all the known constructions that offer almost-surely terminating feature. With the resilience of t \textbackslashtextless n/3+ε for any ε \textbackslashtextgreater 0, our second protocol runs for expected O( 1 ε ) time. The expected running time of our protocol turns constant when ε is a constant fraction. The known constructions with constant expected running time either require ε to be at least 1 (Feldman-Micali, STOC 1988), implying t \textbackslashtextless n/4, or calls for exponential computing power from the honest parties (Wang, CoRR 2015). We follow the traditional route of building BA via common coin protocol that in turn reduces to asynchronous verifiable secretsharing (AVSS). Our constructions are built on a variant of AVSS that is termed as shunning. A shunning AVSS fails to offer the properties of AVSS when the corrupt parties strike, but allows the honest parties to locally detect and shun a set of corrupt parties for any future communication. Our shunning AVSS with t \textbackslashtextless n/3 and t \textbackslashtextless n 3+ε guarantee Ω(n) and respectively Ω(εt 2) conflicts to be revealed when failure occurs. Turning this shunning AVSS to a common coin protocol constitutes another contribution of our paper.

Public cloud data storage services were considered as a potential alternative to store low-cost digital data in the short term. They are offered by different providers on the Internet. Some providers offer limited free plans for the users who are starting the service. However, data security concern arises when data stored are considered as a valuable asset. This study explores the usage of secret sharing scheme: Rabin's IDA and Shamir's SSA to implement a tool called dCloud for file protection stored in public cloud storage in a seamless way. It addresses data security by hiding its complexities when targeting ordinary non-technical users. The secret key is automatically generated by dCloud in a secure random way on Rabin's IDA. Shamir's SSA completes the process through dispersing the key into each of Rabin's IDA output files. Moreover, the Hash value of the original file is added to each of those output files to confirm the integrity of the file during reconstruction. Besides, the authentication key is used to communicate with all of the defined service providers during storage and reconstruction as well. It is stored into local secure key-store. By having a key to access the key-store, an ordinary non-technical user will be able to use dCloud to store and retrieve targeted file within defined public cloud storage services securely.

We study secret sharing schemes for general (non-threshold) access structures. A general secret sharing scheme for n parties is associated to a monotone function F:\0,1\n$\rightarrow$\0,1\. In such a scheme, a dealer distributes shares of a secret s among n parties. Any subset of parties T $\subseteq$ [n] should be able to put together their shares and reconstruct the secret s if F(T)=1, and should have no information about s if F(T)=0. One of the major long-standing questions in information-theoretic cryptography is to minimize the (total) size of the shares in a secret-sharing scheme for arbitrary monotone functions F. There is a large gap between lower and upper bounds for secret sharing. The best known scheme for general F has shares of size 2n-o(n), but the best lower bound is $Ømega$(n2/logn). Indeed, the exponential share size is a direct result of the fact that in all known secret-sharing schemes, the share size grows with the size of a circuit (or formula, or monotone span program) for F. Indeed, several researchers have suggested the existence of a representation size barrier which implies that the right answer is closer to the upper bound, namely, 2n-o(n). In this work, we overcome this barrier by constructing a secret sharing scheme for any access structure with shares of size 20.994n and a linear secret sharing scheme for any access structure with shares of size 20.999n. As a contribution of independent interest, we also construct a secret sharing scheme with shares of size 2Õ($\surd$n) for 2n n/2 monotone access structures, out of a total of 2n n/2$\cdot$ (1+O(logn/n)) of them. Our construction builds on recent works that construct better protocols for the conditional disclosure of secrets (CDS) problem.

Recently, there has been a growing interest in using online technologies to design protocols for secure electronic voting. The main challenges include vote privacy and anonymity, ballot irrevocability and transparency throughout the vote counting process. The introduction of the blockchain as a basis for cryptocurrency protocols, provides for the exploitation of the immutability and transparency properties of these distributed ledgers. In this paper, we discuss possible uses of the blockchain technology to implement a secure and fair voting system. In particular, we introduce a secret share-based voting system on the blockchain, the so-called SHARVOT protocol1. Our solution uses Shamir's Secret Sharing to enable on-chain, i.e. within the transactions script, votes submission and winning candidate determination. The protocol is also using a shuffling technique, Circle Shuffle, to de-link voters from their submissions.

We provide a systemization of knowledge of the recent progress made in addressing the crucial problem of deep learning on encrypted data. The problem is important due to the prevalence of deep learning models across various applications, and privacy concerns over the exposure of deep learning IP and user's data. Our focus is on provably secure methodologies that rely on cryptographic primitives and not trusted third parties/platforms. Computational intensity of the learning models, together with the complexity of realization of the cryptography algorithms hinder the practical implementation a challenge. We provide a summary of the state-of-the-art, comparison of the existing solutions, as well as future challenges and opportunities.

Currently, when companies conduct risk analysis of own networks and systems, it is common to outsource risk analysis to third-party experts. At that time, the company passes the information used for risk analysis including confidential information such as network configuration to third-party expert. It raises the risk of leakage and abuse of confidential information. Therefore, a method of risk analysis by using secure computation without passing confidential information of company has been proposed. Although Liu's method have firstly achieved secure risk analysis method using multiparty computation and attack tree analysis, it has several problems to be practical. In this paper, improvement of secure risk analysis method is proposed. It can dynamically reduce compilation time, enhance scale of target network and system without increasing execution time. Experimental work is carried out by prototype implementation. As a result, we achieved improved performance in compile time and enhance scale of target with equivalent performance on execution time.

The privacy of information is an increasing concern of software applications users. This concern was caused by attacks to cloud services over the last few years, that have leaked confidential information such as passwords, emails and even private pictures. Once the information is leaked, the users and software applications are powerless to contain the spread of information and its misuse. With databases as a central component of applications that store almost all of their data, they are one of the most common targets of attacks. However, typical deployments of databases do not leverage security mechanisms to stop attacks and do not apply cryptographic schemes to protect data. This issue has been tackled by multiple secure databases that provide trade-offs between security, query capabilities and performance. Despite providing stronger security guarantees, the proposed solutions still entrust their data to a single entity that can be corrupted or hacked. Secret sharing can solve this problem by dividing data in multiple secrets and storing each secret at a different location. The division is done in such a way that if one location is hacked, no information can be leaked. Depending on the protocols used to divide data, functions can be computed over this data through secure protocols that do not disclose information or actually know which values are being calculated. We propose a SQL database prototype capable of offering a trade-off between security and query latency by using a different secure protocol. An evaluation of the protocols is also performed, showing that our most relaxed protocol has an improvement of 5+ on the query latency time over the original protocol.

Oblivious Random Access Machine (ORAM) enables a client to access her data without leaking her access patterns. Existing client-efficient ORAMs either achieve O(log N) client-server communication blowup without heavy computation, or O(1) blowup but with expensive homomorphic encryptions. It has been shown that O(log N) bandwidth blowup might not be practical for certain applications, while schemes with O(1) communication blowup incur even more delay due to costly homomorphic operations. In this paper, we propose a new distributed ORAM scheme referred to as Shamir Secret Sharing ORAM (S3ORAM), which achieves O(1) client-server bandwidth blowup and O(1) blocks of client storage without relying on costly partial homomorphic encryptions. S3ORAM harnesses Shamir Secret Sharing, tree-based ORAM structure and a secure multi-party multiplication protocol to eliminate costly homomorphic operations and, therefore, achieves O(1) client-server bandwidth blowup with a high computational efficiency. We conducted comprehensive experiments to assess the performance of S3ORAM and its counterparts on actual cloud environments, and showed that S3ORAM achieves three orders of magnitude lower end-to-end delay compared to alternatives with O(1) client communication blowup (Onion-ORAM), while it is one order of magnitude faster than Path-ORAM for a network with a moderate bandwidth quality. We have released the implementation of S3ORAM for further improvement and adaptation.

In smart grid, large quantities of data is collected from various applications, such as smart metering substation state monitoring, electric energy data acquisition, and smart home. Big data acquired in smart grid applications is usually sensitive. For instance, in order to dispatch accurately and support the dynamic price, lots of smart meters are installed at user's house to collect the real-time data, but all these collected data are related to user privacy. In this paper, we propose a data aggregation scheme based on secret sharing with fault tolerance in smart grid, which ensures that control center gets the integrated data without revealing user's privacy. Meanwhile, we also consider fault tolerance during the data aggregation. At last, we analyze the security of our scheme and carry out experiments to validate the results.

The Internet of Things is a disruptive paradigm based on the cooperation of a plethora of heterogeneous smart things to collect, transmit, and analyze data from the ambient environment. To this end, many monitored variables are combined by a data analysis module in order to implement efficient context-aware decision mechanisms. To ensure resource efficiency, aggregation is a long established solution, however it is applicable only in the case of one sensed variable. We extend the use of aggregation to the complex context of IoT by proposing a novel approach for secure cooperation of smart things while granting confidentiality and integrity. Traditional solutions for data concealment in resource constrained devices rely on hop-by-hop or end-to-end encryption, which are shown to be inefficient in our context. We use a more sophisticated scheme relying on homomorphic encryption which is not compromise resilient. We combine fully additive encryption with fully additive secret sharing to fulfill the required properties. Thorough security analysis and performance evaluation show a viable tradeoff between security and efficiency for our scheme.

We consider the problem of privacy-preserving data aggregation in a star network topology, i.e., several untrusting participants connected to a single aggregator. We require that the participants do not discover each other's data, and the service provider remains oblivious to each participant's individual contribution. Furthermore, the final result is to be published in a differentially private manner, i.e., the result should not reveal the contribution of any single participant to a (possibly external) adversary who knows the contributions of all other participants. In other words, we require a secure multiparty computation protocol that also incorporates a differentially private mechanism. Previous solutions have resorted to caveats such as postulating a trusted dealer to distribute keys to the participants, or introducing additional entities to withhold the decryption key from the aggregator, or relaxing the star topology by allowing pairwise communication amongst the participants. In this paper, we show how to obtain a noisy (differentially private) aggregation result using Shamir secret sharing and additively homomorphic encryption without these mitigating assumptions. More importantly, while we assume semi-honest participants, we allow the aggregator to be stronger than semi-honest, specifically in the sense that he can try to reduce the noise in the differentially private result. To respect the differential privacy requirement, collusions of mutually untrusting entities need to be analyzed differently from traditional secure multiparty computation: It is not sufficient that such collusions do not reveal the data of honest participants; we must also ensure that the colluding entities cannot undermine differential privacy by reducing the amount of noise in the final result. Our protocols avoid this by requiring that no entity – neither the aggregator nor any participant – knows how much noise a participant contributes to the final result. We also ensure that if a cheating aggregator tries to influence the noise term in the differentially private output, he can be detected with overwhelming probability.

Chang-Chen-Wang's (3,n) Secret grayscale image Sharing between n grayscale cover images method with participant Authentication and damaged pixels Repairing (SSAR) properties is analyzed; it restores the secret image from any three of the cover images used. We show that SSAR may fail, is not able fake participant recognizing, and has limited by 62.5% repairing ability. We propose SSAR (4,n) enhancement, SSAR-E, allowing 100% exact restoration of a corrupted pixel using any four of n covers, and recognizing a fake participant with the help of cryptographic hash functions with 5-bit values that allows better (vs. 4 bits) error detection. Using a special permutation with only one loop including all the secret image pixels, SSAR-E is able restoring all the secret image damaged pixels having just one correct pixel left. SSAR-E allows restoring the secret image to authorized parties only contrary to SSAR. The performance and size of cover images for SSAR-E are the same as for SSAR.

This paper presents SplitBox, an efficient system for privacy-preserving processing of network functions that are outsourced as software processes to the cloud. Specifically, cloud providers processing the network functions do not learn the network policies instructing how the functions are to be processed. First, we propose an abstract model of a generic network function based on match-action pairs. We assume that this function is processed in a distributed manner by multiple honest-but-curious cloud service providers. Then, we introduce our SplitBox system for private network function virtualization and present a proof-of-concept implementation on FastClick, an extension of the Click modular router, using a firewall as a use case. Our experimental results achieve a throughput of over 2 Gbps with 1 kB-sized packets on average, traversing up to 60 firewall rules.

Polynomial masking is a glitch-resistant and higher-order masking scheme based upon Shamir's secret sharing scheme and multi-party computation protocols. Polynomial masking was first introduced at CHES 2011, while a 1st-order implementation of the AES S-box on FPGA was presented at CHES 2013. In this latter work, the authors showed a 2nd-order univariate leakage by side-channel collision analysis on a tuned measurement setup. This negative result motivates the need to evaluate the performance, area-costs, and security margins of combined \shuffled\ and higher-order polynomially masking schemes to counteract trivial univariate leakages. In this work, we provide the following contributions: first, we introduce additional principles for the selection of efficient addition chains, which allow for more compact and faster implementations of cryptographic S-boxes. Our 1st-order AES S-box implementation requires approximately 27% less registers, 20% less clock cycles, and 5% less random bits than the CHES 2013 implementation. Then, we propose a lightweight shuffling countermeasure, which inherently applies to polynomial masking schemes and effectively enhances their univariate security at negligible area expenses. Finally, we present the design of a \combined\ \shuffled\ \and\ higher-order polynomially masked AES S-box in hardware, while providing ASIC synthesis and side-channel analysis results in the Electro-Magnetic (EM) domain.

Chang-Chen-Wang's (3,n) Secret grayscale image Sharing between n grayscale cover images method with participant Authentication and damaged pixels Repairing (SSAR) properties is analyzed; it restores the secret image from any three of the cover images used. We show that SSAR may fail, is not able fake participant recognizing, and has limited by 62.5% repairing ability. We propose SSAR (4,n) enhancement, SSAR-E, allowing 100% exact restoration of a corrupted pixel using any four of n covers, and recognizing a fake participant with the help of cryptographic hash functions with 5-bit values that allows better (vs. 4 bits) error detection. Using a special permutation with only one loop including all the secret image pixels, SSAR-E is able restoring all the secret image damaged pixels having just one correct pixel left. SSAR-E allows restoring the secret image to authorized parties only contrary to SSAR. The performance and size of cover images for SSAR-E are the same as for SSAR.