Privacy Preserving Reputation Calculation in P2P Systems with Homomorphic Encryption

2021 ◽  
Vol 13 (6) ◽  
pp. 1-18
Author(s):  
FUJITA Satoshi
Keyword(s):  
Linear System ◽  
Homomorphic Encryption ◽  
Distributed Processing ◽  
Reputation Management ◽  
Partial Knowledge ◽  
Secret Key ◽  
Trust Value ◽  
P2p Systems ◽  
Public Keys ◽  
P2p System

In this paper, we consider the problem of calculating the node reputation in a Peer-toPeer (P2P) system from fragments of partial knowledge concerned with the trustfulness of nodes which are subjectively given by each node (i.e., evaluator) participating in the system. We are particularly interested in the distributed processing of the calculation of reputation scores while preserving the privacy of evaluators. The basic idea of the proposed method is to extend the EigenTrust reputation management system with the notion of homomorphic cryptosystem. More specifically, it calculates the main eigenvector of a linear system which models the trustfulness of the users (nodes) in the P2P system in a distributed manner, in such a way that: 1) it blocks accesses to the trust value by the nodes to have the secret key used for the decryption, 2) it improves the efficiency of calculation by offloading a part of the task to the participating nodes, and 3) it uses different public keys during the calculation to improve the robustness against the leave of nodes. The performance of the proposed method is evaluated through numerical calculations.

Towards Semantic-Based P2P Reputation Systems

2011 ◽  
pp. 101-119
Author(s):  
Ernesto Damiani ◽  
Marco Viviani
Keyword(s):  
Overlay Networks ◽  
Research Effort ◽  
Peer To Peer ◽  
Reputation Systems ◽  
Shared Resources ◽  
Trust Value ◽  
P2p Systems ◽  
Data Objects ◽  
P2p System ◽  
Simple Features

Peer-to-peer (P2P) systems represent nowadays a large portion of Internet traffic, and are fundamental data sources. In a pure P2P system, since no peer has the power or responsibility to monitor and restrain others behaviours, there is no method to verify the trustworthiness of shared resources, and malicious peers can spread untrustworthy data objects to the system. Furthermore, data descriptions are often simple features directly connected to data or annotations based on heterogeneous schemas, a fact that makes difficult to obtain a single coherent trust value on a resource. This chapter describes techniques where the combination of Semantic Web and peer-to-peer technologies is used for expressing the knowledge shared by peers in a well-defined and formal way. Finally, dealing with Semantic-based P2P networks, the chapter suggests a research effort in this direction, where the association between cluster-based overlay networks and reputation systems based on numerical approaches seems to be promising.

scholarly journals Compressible Multikey and Multi-Identity Fully Homomorphic Encryption

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Tongchen Shen ◽  
Fuqun Wang ◽  
Kefei Chen ◽  
Zhonghua Shen ◽  
Renjun Zhang
Keyword(s):  
Key Management ◽  
Homomorphic Encryption ◽  
Third Party ◽  
Secret Key ◽  
Fully Homomorphic Encryption ◽  
Expansion Technique ◽  
Public Keys ◽  
Learning With Errors ◽  
Computable Functions ◽  
Computing Models

With the development of new computing models such as cloud computing, user’s data are at the risk of being leaked. Fully homomorphic encryption (FHE) provides a possible way to fundamentally solve the problem. It enables a third party who does not know anything about the secret key and plaintexts to homomorphically perform any computable functions on the corresponding ciphertexts. In 2009, Gentry proposed the first FHE scheme. After that, its inefficiency has always been a bottleneck of the development of practical schemes and applications. At TCC 2019, Gentry and Halevi proposed the first compressible FHE scheme that enables the ratio of plaintext size to the ciphertext size (i.e., the compression rate) to reach 1 − ε for any small ε > 0 under the standard learning with errors (LWE) assumption. However, it is only a single-key one, where the homomorphic evaluation can only be performed over ciphertexts encrypted under the same key. Compared with single-key FHE, multikey FHE is more practical. Multikey FHE enables ciphertexts encrypted under different public keys to be homomorphically computed without having to decrypt these ciphertexts using their own private keys. In addition, in a multi-identity FHE scheme, only identity information and public parameters are required when encrypting, which simplifies certificate-based key management in public key infrastructure. In this paper, a new compressible ciphertext expansion technique is proposed. Then, we use this technique to construct a compressible multikey FHE scheme and a compressible multi-identity FHE scheme to overcome the bottleneck of bandwidth inefficiency in the multikey and multi-identity settings. The two schemes proposed in this paper make it possible that the objects of homomorphic operation can be the ciphertexts encrypted under different keys or different identities before compression, thus solving the single-key defect of the work of Gentry and Halevi.

Computing Blindfolded on Data Homomorphically Encrypted under Multiple Keys: A Survey

2022 ◽  
Vol 54 (9) ◽  
pp. 1-37
Author(s):  
Asma Aloufi ◽  
Peizhao Hu ◽  
Yongsoo Song ◽  
Kristin Lauter
Keyword(s):  
Homomorphic Encryption ◽  
Lessons Learned ◽  
Secret Key ◽  
Sensitive Data ◽  
Encrypted Data ◽  
Outsourced Computation ◽  
Secret Keys ◽  
Comprehensive Survey ◽  
Multiple Keys ◽  
Cryptographic Techniques

With capability of performing computations on encrypted data without needing the secret key, homomorphic encryption (HE) is a promising cryptographic technique that makes outsourced computations secure and privacy-preserving. A decade after Gentry’s breakthrough discovery of how we might support arbitrary computations on encrypted data, many studies followed and improved various aspects of HE, such as faster bootstrapping and ciphertext packing. However, the topic of how to support secure computations on ciphertexts encrypted under multiple keys does not receive enough attention. This capability is crucial in many application scenarios where data owners want to engage in joint computations and are preferred to protect their sensitive data under their own secret keys. Enabling this capability is a non-trivial task. In this article, we present a comprehensive survey of the state-of-the-art multi-key techniques and schemes that target different systems and threat models. In particular, we review recent constructions based on Threshold Homomorphic Encryption (ThHE) and Multi-Key Homomorphic Encryption (MKHE). We analyze these cryptographic techniques and schemes based on a new secure outsourced computation model and examine their complexities. We share lessons learned and draw observations for designing better schemes with reduced overheads.

Design Of Hybrid Cryptography With Vigenere Cipher And RSA Algorithm On IOT Data Security

2020 ◽  
Vol 8 (4) ◽  
pp. 475
Author(s):  
Maria Okta Safira ◽  
I Komang Ari Mogi
Keyword(s):  
System Architecture ◽  
Data Security ◽  
Data Encryption ◽  
Public Key ◽  
Secret Message ◽  
Secret Key ◽  
Rsa Algorithm ◽  
Tcp Protocol ◽  
Public Keys ◽  
Encryption And Decryption

In this paper two methods are used, namely the vigenere cipher method and the RSA method. The vigenere cipher method is an example of a symmetric algorithm, while RSA is an example of an asymmetric algorithm. The combination of these two methods is called hybrid cryptography which has the advantage in terms of speed during the encryption process. Each process, which is encryption and decryption, is carried out twice, so that security can be ensured. In the process of forming the key used the RSA method. In the encryption process using public keys that have been generated before when the key is formed. This public key is used in sending data to the recipient of a secret message where this key is used for the data encryption process. The Secret key is kept and will be used during the decryption process. There is a system architecture that describes how clients and servers communicate with each other over the internet using the TCP protocol where the client here is an IoT device and the server is a server. 

On Securing Cloud Storage Using a Homomorphic Framework

2018 ◽  
pp. 99-114
Author(s):  
Daya Sagar Gupta ◽  
G. P. Biswas
Keyword(s):  
Elliptic Curve ◽  
Cloud Storage ◽  
Homomorphic Encryption ◽  
Cloud Security ◽  
Public Key ◽  
Cloud System ◽  
Secret Message ◽  
Security Mechanism ◽  
Secret Key ◽  
Hard Problems

In this chapter, a cloud security mechanism is described in which the computation (addition) of messages securely stored on the cloud is possible. Any user encrypts the secret message using the receiver's public key and stores it. Later on, whenever the stored message is required by an authentic user, he retrieves the encrypted message and decrypts it by using his secret key. However, he can also request the cloud for an addition of encrypted messages. The cloud system only computes the requested addition and sends it to the authentic user; it cannot decrypt the stored encrypted messages on its own. This addition of encrypted messages should be the same as the encryption of the addition of original messages. In this chapter, the authors propose a homomorphic encryption technique in which the above-discussed scenario is possible. The cloud securely computes the addition of the encrypted messages which is ultimately the encryption of the addition of the original messages. The security of the proposed encryption technique depends on the hardness of elliptic curve hard problems.

Peer-to-Peer Usage Analysis

2009 ◽  
pp. 1136-1141
Author(s):  
Florent Masseglia ◽  
Pascal Poncelet ◽  
Maguelonne Teisseire
Keyword(s):  
Data Mining ◽  
Distributed Database ◽  
Peer To Peer ◽  
Communication Model ◽  
P2p Systems ◽  
P2p File Sharing ◽  
Limited Edition ◽  
Usage Analysis ◽  
High Dynamics ◽  
P2p System

With the huge number of information sources available on the Internet and the high dynamics of their data, peer-to-peer (P2P) systems propose a communication model in which each party has the same capabilities and can initiate a communication session. These networks allow a group of computer users with the same networking program to connect with each other and directly access resources from one another. P2P architectures also provide a good infrastructure for data and computer intensive operations such as data mining. In this article we consider a new data mining approach for improving resource searching in a dynamic and distributed database such as an unstructured P2P system, that is, in Masseglia, Poncelet, and Teisseire (2006) we call this problem P2P usage analysis. More precisely we aim at discovering frequent behaviors among users of such a system. We will focus on the sequential order between actions performed on each node (requests or downloads) and show how this order has to be taken into account for extracting useful knowledge. For instance, it may be discovered, in a P2P file sharing network that for 77% of nodes from which a request is sent for “Mandriva Linux,” the file “Mandriva Linux 2005 CD1 i585-Limited- Edition-Mini.iso” is chosen and downloaded; then a new request is performed with the possible name of the remaining iso images (i.e., “Mandriva Linux 2005 Limited Edition”), and in the large number of returned results the image corresponding to “Mandriva Linux 2005 CD2 i585-Limited-Edition-Mini.iso” is chosen and downloaded. Such knowledge is very useful for proposing the user with often downloaded or requested files according to a majority of behaviors. It could also be useful in order to avoid extra bandwidth consumption, which is the main cost of P2P queries (Ng, Chu, Rao, Sripanidkulchai, & Zhang, 2003).

A Novel Data Consistence Model Based on Virtual Peers in Peer-to-Peer Systems

2020 ◽  
Vol 16 (3) ◽  
pp. 1-16
Author(s):  
Hong He
Keyword(s):  
Data Retrieval ◽  
Peer To Peer ◽  
Data Consistency ◽  
P2p Systems ◽  
Efficient Storage ◽  
Peer To Peer Systems ◽  
Consistency Problem ◽  
Unstructured P2p ◽  
Effectiveness And Efficiency ◽  
P2p System

In recent years, peer-to-peer (P2P) systems have become a promising paradigm to provide efficient storage service in distributed environments. Although its effectiveness has been proven in many areas, the data consistency problem in P2P systems are still an opening issue. This article proposes a novel data consistence model, virtual peers-based data consistency (VPDC), which introduces a set of virtual peers to provide guaranteed data consistency in decentralized and unstructured P2P systems. The VPDC model can be easily implemented in any P2P system without introducing any interference to data retrieval. Theoretical analysis on VPDC is presented to analyze its effectiveness and efficiency, and massive experiments are conducted to evaluate the performance of a VPDC model in a real-world P2P system. The results indicate that it can significantly improve the data consistence of P2P systems and outperform many similar approaches in various experimental settings.

scholarly journals LWR-Based Fully Homomorphic Encryption, Revisited

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fucai Luo ◽  
Fuqun Wang ◽  
Kunpeng Wang ◽  
Jie Li ◽  
Kefei Chen
Keyword(s):  
Tensor Product ◽  
Gaussian Noise ◽  
Homomorphic Encryption ◽  
Matrix Multiplication ◽  
Public Key ◽  
Multiparty Computation ◽  
Secret Key ◽  
Fully Homomorphic Encryption ◽  
Private Key ◽  
Modulus Problem

Very recently, Costache and Smart proposed a fully homomorphic encryption (FHE) scheme based on the Learning with Rounding (LWR) problem, which removes the noise (typically, Gaussian noise) sampling needed in the previous lattices-based FHEs. But their scheme did not work, since the noise of homomorphic multiplication is complicated and large, which leads to failure of decryption. More specifically, they chose LWR instances as a public key and the private key therein as a secret key and then used the tensor product to implement homomorphic multiplication, which resulted in a tangly modulus problem. Recall that there are two moduli in the LWR instances, and then the moduli will tangle together due to the tensor product. Inspired by their work, we built the first workable LWR-based FHE scheme eliminating the tangly modulus problem by cleverly adopting the celebrated approximate eigenvector method proposed by Gentry et al. at Crypto 2013. Roughly speaking, we use a specific matrix multiplication to perform the homomorphic multiplication, hence no tangly modulus problem. Furthermore, we also extend the LWR-based FHE scheme to the multikey setting using the tricks used to construct LWE-based multikey FHE by Mukherjee and Wichs at Eurocrypt 2016. Our LWR-based multikey FHE construction provides an alternative to the existing multikey FHEs and can also be applied to multiparty computation with higher efficiency.

scholarly journals Homomorphic Comparison for Point Numbers with User-Controllable Precision and Its Applications

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 788
Author(s):  
Heewon Chung ◽  
Myungsun Kim ◽  
Ahmad Al Badawi ◽  
Khin Mi Mi Aung ◽  
Bharadwaj Veeravalli
Keyword(s):  
Continued Fraction ◽  
Homomorphic Encryption ◽  
Data Types ◽  
Real Numbers ◽  
The Public ◽  
Public Keys ◽  
Real World Applications ◽  
User Data ◽  
Partial Quotients ◽  
And Storage

This work is mainly interested in ensuring users’ privacy in asymmetric computing, such as cloud computing. In particular, because lots of user data are expressed in non-integer data types, privacy-enhanced applications built on fully homomorphic encryption (FHE) must support real-valued comparisons due to the ubiquity of real numbers in real-world applications. However, as FHE schemes operate in specific domains, such as that of congruent integers, most FHE-based solutions focus only on homomorphic comparisons of integers. Attempts to overcome this barrier can be grouped into two classes. Given point numbers in the form of approximate real numbers, one class of solution uses a special-purpose encoding to represent the point numbers, whereas the other class constructs a dedicated FHE scheme to encrypt point numbers directly. The solutions in the former class may provide depth-efficient arithmetic (i.e., logarithmic depth in the size of the data), but not depth-efficient comparisons between FHE-encrypted point numbers. The second class may avoid this problem, but it requires the precision of point numbers to be determined before the FHE setup is run. Thus, the precision of the data cannot be controlled once the setup is complete. Furthermore, because the precision accuracy is closely related to the sizes of the encryption parameters, increasing the precision of point numbers results in increasing the sizes of the FHE parameters, which increases the sizes of the public keys and ciphertexts, incurring more expensive computation and storage. Unfortunately, this problem also occurs in many of the proposals that fall into the first class. In this work, we are interested in depth-efficient comparison over FHE-encrypted point numbers. In particular, we focus on enabling the precision of point numbers to be manipulated after the system parameters of the underlying FHE scheme are determined, and even after the point numbers are encrypted. To this end, we encode point numbers in continued fraction (CF) form. Therefore, our work lies in the first class of solutions, except that our CF-based approach allows depth-efficient homomorphic comparisons (more precisely, the complexity of the comparison is O ( log κ + log n ) for a number of partial quotients n and their bit length κ , which is normally small) while allowing users to determine the precision of the encrypted point numbers when running their applications. We develop several useful applications (e.g., sorting) that leverage our CF-based homomorphic comparisons.

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