Boneh-Goh-Nissim Research Articles

BGNBA-OCO based privacy preserving attribute based access control with data duplication for secure storage in cloud

Cloud computing technology offers flexible and expedient services that carry a variety of profits for both societies as well as individuals. De-duplication techniques were developed to minimize redundant data in the cloud storage. But, one of the main challenges of cloud storage is data deduplication with secure data storage.To overcome the issue, we propose Boneh Goh Nissim Bilinear Attribute-based Optimal Cache Oblivious (BGNBA-OCO) access control and secure de-duplication for data storage in cloud computing in this paper. The proposed method achieves fne-grained access control with low computation consumption. We design Boneh Goh Nissim Privacy Preserving Revocable Attribute-based Encryption that reinforces attribute revocation and averts the discharge of sensitive information. Furthermore, we utilize Optimal Cache Oblivious algorithm to prevent disclosure of access patterns to hide the access patterns in cloud storage via rand pattern matching. We support updating both encrypted data and access control policies to minimize communication and computation overhead of data duplication and encryption processes concurrently. We perform secure data sharing to achieve higher data confidentiality and integrity. Finally, we conducted the extensive experiments in cloud and the results illustrated that our proposed BGNBA-OCO method is more efficient than related works.

L-PRNU: Low-Complexity Privacy-Preserving PRNU-Based Camera Attribution Scheme

A personal camera fingerprint can be created from images in social media by using Photo Response Non-Uniformity (PRNU) noise, which is used to identify whether an unknown picture belongs to them. Social media has become ubiquitous in recent years and many of us regularly share photos of our daily lives online. However, due to the ease of creating a PRNU-based camera fingerprint, the privacy leakage problem is taken more seriously. To address this issue, a security scheme based on Boneh–Goh–Nissim (BGN) encryption was proposed in 2021. While effective, the BGN encryption incurs a high run-time computational overhead due to its power computation. Therefore, we devised a new scheme to address this issue, employing polynomial encryption and pixel confusion methods, resulting in a computation time that is over ten times faster than BGN encryption. This eliminates the need to only send critical pixels to a Third-Party Expert in the previous method. Furthermore, our scheme does not require decryption, as polynomial encryption and pixel confusion do not alter the correlation value. Consequently, the scheme we presented surpasses previous methods in both theoretical analysis and experimental performance, being faster and more capable.

Lattice-Based Homomorphic Encryption For Privacy-Preserving Smart Meter Data Analytics

Abstract Privacy-preserving smart meter data collection and analysis are critical for optimizing smart grid environments without compromising privacy. Using homomorphic encryption techniques, smart meters can encrypt collected data to ensure confidentiality, and other untrusted nodes can further compute over the encrypted data without having to recover the underlying plaintext. As an illustrative example, this approach can be useful to compute the monthly electricity consumption without violating consumer privacy by collecting fine-granular data through small increments of time. Toward that end, we propose an architecture for privacy-preserving smart meter data collection, aggregation and analysis based on lattice-based homomorphic encryption. Furthermore, we compare the proposed method with the Paillier and Boneh–Goh–Nissim (BGN) cryptosystems, which are popular alternatives for homomorphic encryption in smart grids. We consider different services with different requirements in terms of multiplicative depth, e.g. billing, variance and nonlinear support vector machine classification. Accordingly, we measure and show the practical overhead of using the proposed homomorphic encryption method in terms of communication traffic (ciphertext size) and latency. Our results show that lattice-based homomorphic encryption is more efficient than Paillier and BGN for both multiplication and addition operations while offering more flexibility in terms of the computation that can be evaluated homomorphically.

Homomorphic additive lightweight block cipher scheme for securing IoT applications

Recent emerging systems and applications may be able to process encrypted data to preserve user privacy. However, designing an efficient Homomorphic Encryption (HE) algorithm that can achieve good performance and robustness against attacks is primordial to better respond to real-time requirements and tiny devices. Unfortunately, existing homomorphic asymmetric schemes suffer from high computational complexity, storage, and communication overhead. On the other hand, homomorphic symmetric approaches suffer from a low level of security, and a big majority of them suffer from weaknesses in defending against known plain-text/cipher-text attacks. In this paper, an efficient symmetric homomorphic block cipher scheme is proposed to allow the performing of addition and average operations over cipher-texts. It defines a simple round function that should be iterated for nr≥2 iterations. Moreover, the dynamic key approach is used in this solution to reach a high level of security. To the best of our knowledge, it is a new symmetric additive homomorphic block cipher structure, and it defines three novel variants of addition round keys and two novel variants of the diffusion matrix. Furthermore, the proposed solution is analyzed in detail and evaluated in terms of security and performance levels. In comparison with well-known asymmetric additive HE schemes such as Paillier, BGN (Boneh, Goh, Nissim), BCP (Bresson, Catalano, Pointcheval), and ElGamal ECC (Elliptic Curve ), the obtained results prove that the proposed solution reaches a good balance between both performance and security levels. Therefore, the proposed solution can be applied on limited devices and especially for real-time scenarios that require the additive homomorphic property and the average calculation over cipher-texts.

Privacy-preserving statistical analysis over multi-dimensional aggregated data in edge computing-based smart grid systems

Smart grid systems enable bidirectional data communication between users and a smart grid control center (CC), by utilizing various communication infrastructures and embedded devices. To extract valuable information from users’ power consumption data efficiently, multi-dimensional data of users are required to be analyzed deeply. To protect users’ privacy, power consumption data are usually encrypted before transmission, which simultaneously makes it difficult to conduct statistical analysis. In this paper, we propose a scheme which enables privacy-preserving statistical analysis over multi-dimensional aggregated data (SA-MAD) in smart grid systems equipped with edge computing. We modify Boneh–Goh–Nissim (BGN) public key cryptosystem to a dual-message encryption mode, combining with two special superincreasing sequences to deal with multi-dimensional encrypted data aggregation. Besides, we design an identity-based aggregate signature to ensure encrypted data integrity in smart grid systems, and employ shamir secret sharing technique to support transmission fault-tolerance mechanism from smart meters to corresponding edge servers. SA-MAD enables CC to flexibly conduct privacy-preserving statistical analysis (e.g., sum, average, and variance) over aggregated data, and it could be easily extended to support covariance and linear regression computation. The performance evaluation demonstrates the feasibility of SA-MAD in edge computing-based smart grid systems.

CrypSH: A Novel IoT Data Protection Scheme Based on BGN Cryptosystem

The Internet of Things (IoT) is an emerging paradigm and has penetrated deeply into our daily life. Due to the seamless connections of the IoT devices with the physical world through the Internet, the IoT applications use the cloud to store and provide ubiquitous access to collected data. Sharing of data with third party services and other users incurs potential risks and leads to unique security and privacy concerns, e.g., data breaches. Existing cryptographic solutions are inapt for resource-constrained IoT devices, because of their significant computational overhead. To address these concerns, we propose a data protection scheme to store the encrypted IoT data in a cloud, while still allowing query processing over the encrypted data. Our proposed scheme features a novel encrypted data sharing scheme based on Boneh-Goh-Nissim (BGN) cryptosystem, with revocation capabilities and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> key updates. We perform exhaustive experiments on real datasets, to assess the feasibility of the proposed scheme on the resource constrained IoT devices. The results show the feasibility of our scheme, together with the ability to provide a high level of security. The results also show that our scheme significantly reduces the computation, storage and energy overheads than the best performed scheme in the state-of-the-art.

E-PRNU: Encrypted Domain PRNU-Based Camera Attribution for Preserving Privacy

Photo Response Non-Uniformity (PRNU) noise-based source camera attribution is a popular digital forensic method. In this method, a camera fingerprint computed from a set of known images of the camera is matched against the extracted noise of an anonymous questionable image to find out if the camera had taken the anonymous image. The possibility of privacy leak, however, is one of the main concerns of the PRNU-based method. Using the camera fingerprint (or the extracted noise), an adversary can identify the owner of the camera by matching the fingerprint with the noise of an image (or with the fingerprint computed from a set of images) crawled from a social media account. In this article, we address this privacy concern by encrypting both the fingerprint and the noise using the Boneh-Goh-Nissim (BGN) encryption scheme, and performing the matching in encrypted domain. To overcome leakage of privacy from the content of an image that is used in the fingerprint calculation, we compute the fingerprint within a trusted environment, such as ARM TrustZone. We present e-PRNU that aims at minimizing privacy loss and allows authorized forensic experts to perform camera attribution. The security analysis shows that the proposed approach is semantically secure. Experimental results show that the run-time computational overhead is 10.26 seconds when a cluster of 64 computing nodes are used.

Key-Leakage Resilient Encrypted Data Aggregation With Lightweight Verification in Fog-Assisted Smart Grids

In this article, we analyze the inherent characteristics of smart grids, and point out that some electricity consumption data are very sensitive and should be encrypted. However, once the corresponding private key is compromised, the content of encrypted data would be leaked, thereby violating users' privacy. Additionally, since a control center (CC) is always required to conduct accurate statistic analysis on these data for subsequent services, it is highly demanded for CC to check the integrity of encrypted data. To this end, based on a modified Boneh-Goh-Nissim (BGN) cryptosystem, we propose a key-leakage resilient encrypted data aggregation (KLR-EDA) scheme with lightweight verification in fog-assisted smart grids. KLR-EDA enables each fog node to aggregate first-level verifiable encrypted data from smart meters in the same grid area, and forward them to the cloud server (CS) for long-term storage. Upon receiving flexible challenging list of fog nodes from CC, CS produces second-level verifiable encrypted aggregated data and returns the results to CC. KLR-EDA enables CC to check the integrity of encrypted aggregated data efficiently, and further obtain the statistic analysis results on the aggregated data without learning any information of individual user. In particular, even the private key of CC is exposed or compromised, any adversary cannot break users' privacy. We provide security analysis of KLR-EDA, and conduct performance evaluation to demonstrate its lightweight statistical analysis and verification advantages on the CC side.

Secure Data Aggregation of Lightweight E-Healthcare IoT Devices With Fair Incentives

With rapid development of e-healthcare systems, patients that are equipped with resource-limited e-healthcare devices (Internet of Things) generate huge amount of health data for health management. These health data possess significant medical value when aggregated from these distributed devices. However, efficient health data aggregation poses several security and privacy issues such as confidentiality disclosure and differential attacks, as well as patients may be reluctant to contribute their health data for aggregation. In this paper, we propose a privacy-preserving heath data aggregation scheme that securely collects health data from multiple sources and guarantee fair incentives for contributing patients. Specifically, we employ signature techniques to keep fair incentives for patients. Meanwhile, we add noises into the health data for differential privacy. Furthermore, we combine Boneh–Goh–Nissim cryptosystem and Shamir’s secret sharing to keep data obliviousness security and fault tolerance. Security and privacy discussions show that our scheme can resist differential attacks, tolerate healthcare centers failures, and keep fair incentives for patients. Performance evaluations demonstrate cost-efficient computation, communication and storage overhead.

Pystin: Enabling Secure LBS in Smart Cities With Privacy-Preserving Top-$k$ Spatial–Textual Query

The convergence of technologies like Cloud computing, mobile, and smart phone technologies has led to the rapid development of location-based services (LBS) in smart cities. For flexibility and cost savings, there is a recent trend to migrate LBS to the Cloud, however it poses a serious threat to the user privacy. In this paper, we present a new privacy preserving top- $k$ spatio-textual keyword ( $\text{T}{k}$ SK) query scheme, called privacy-preserving spatio-textual index (Pystin), which is performed over outsourced Cloud and can enable secure LBS in smart cities. In Pystin, a query user’s accurate location is protected by the combination of Boneh–Goh–Nissim homomorphic encryption and hash bucket techniques, and the privacy of textual information are persevered by a one-way hash function. In addition, a quad-tree-based spatio-textual indexing is integrated into Pystin to further reduce the query latency. Detailed security analyzes show that the proposed Pystin scheme is indeed a privacy-preserving $\text{T}{k}$ SK query scheme. Furthermore, extensive experiments are conducted, and results confirm the scalability, efficiency properties of our proposed Pystin scheme.

Achieving Efficient and Privacy-Preserving Multi-Keyword Conjunctive Query Over Cloud

With the explosive growth of data, it has become increasingly popular to deploy the powerful cloud to manage data. Meanwhile, as the cloud is not always fully trusted, personal and sensitive data have to be encrypted before being outsourced to the cloud. Naturally, this brings a serious challenge for the cloud to provide secure and efficient query services over huge volumes of data. Although existing works have proposed some solutions to solve the above challenge, most of them just focus on the single keyword query and cannot directly support multi-keyword query. Even though some works have discussed solutions for the multi-keyword query, they cannot well balance the efficiency and privacy. Therefore, in this paper, we propose a novel multi-keyword conjunctive query scheme over cloud, which can achieve high query efficiency with small privacy leakage. In specific, we first design a tree-based index to support the multi-keyword conjunctive query and employ Boneh-Goh-Nissim (BGN) homomorphic encryption technique to protect its privacy. Then, based on the tree-based index, we propose a wildcard search algorithm to improve its query efficiency. Finally, the detailed security analysis shows that the proposed scheme is really privacy-preserving, and extensive simulation results also demonstrate its efficient.

Converting pairing-based cryptosystems from composite to prime order setting – A comparative analysis

Abstract Composite order pairing setting has been used to achieve cryptographic functionalities beyond what is attainable in prime order groups. However, such pairings are known to be significantly slower than their prime order counterparts. Thus emerged a new line of research – developing frameworks to convert cryptosystems from composite to prime order pairing setting. In this work, we analyse the intricacies of efficient prime order instantiation of cryptosystems that can be converted using existing frameworks. To compare the relative efficacy of these frameworks we mainly focus on some representative schemes: the Boneh–Goh–Nissim (BGN) homomorphic encryption scheme, ring and group signatures as well as a blind signature scheme. Our concrete analyses lead to several interesting observations. We show that even after a considerable amount of research, the projecting framework implicit in the very first work of Groth–Sahai still remains the best choice for instantiating the BGN cryptosystem. Protocols like the ring signature and group signature which use both projecting and cancelling setting in composite order can be most efficiently instantiated in the Freeman prime-order projecting only setting. In contrast, while the Freeman projecting setting is sufficient for the security reduction of the blind signature scheme, the simultaneous projecting and cancelling setting does provide some efficiency advantage.

Design and implementation of low-depth pairing-based homomorphic encryption scheme

Homomorphic encryption allows to carry out operations on encrypted data. In this paper, we focus on the design of a scheme based on pairings and elliptic curves, that is able to handle applications where the number of multiplication is not too high, with interesting practical efficiency when compared to lattice-based solutions. The starting point is the Boneh–Goh–Nissim (BGN for short) encryption scheme (Boneh et al. in Kilian J (ed) Theory of cryptography, second theory of cryptography conference, TCC 2005, Cambridge, MA, USA, February 10–12, 2005), which enables the homomorphic evaluation of polynomials of degree at most 2 on ciphertexts. In our scheme, we use constructions coming from Freeman (Gilbert H (ed) Advances in cryptology—EUROCRYPT 2010, 29th annual international conference on the theory and applications of cryptographic techniques, French Riviera, May 30–June 3, 2010) and Catalano and Fiore (Ray I, Li N, Kruegel C (eds) Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, Denver, CO, USA, October 12–16, 2015), to propose a variant of the $${\text {BGN}}$$ scheme that can handle the homomorphic evaluation of polynomials of degree at most 4. We discuss both the mathematical structure of the scheme and its implementation. We provide simulation results, showing the relevance of this solution for applications requiring a low multiplicative depth, and give relative comparison with respect to lattice-based homomorphic encryption schemes.

Multidimensional data tight aggregation and fine-grained access control in smart grid

In smart grid, for saving communication bandwidth and performing fine-grained analysis, the power data need to be collected and aggregated in multi-dimensions. As each dimension contains sensitive information of users and may be used by different analytic organizations, privacy preserving and access control for multidimensional power data are obviously important. Existing schemes either cannot support dimensional access control or require transmitting attribute sets of data together with ciphertexts, which leaks the relevant information of power data and increases the security risk. Moreover, to disseminate and implement access control of the multidimensional data, control center (CC) must firstly decrypt the data of all dimensions, which may result in the data leakage. In this paper, we propose a Multidimensional-data Tight Aggregation scheme which supports Privacy preserving and fine-grained Access control (MTA–PA). In MTA–PA, we import the extended Boneh–Goh–Nissim (EBGN) homomorphic encryption from wireless sensor networks to aggregate multidimensional power data into one ciphertext and simultaneously use key-policy attribute-based encryption (KP–ABE) to enforce fine-grained access control at the dimension level. By using MTA–PA, the gateway need not to distinguish different ciphertexts before aggregating them, and organizations only can analyze the authorized dimensions of power data. MTA–PA further achieves secure access-permission update via updating related parameters of EBGN and KP–ABE. And then, we also present the implementation methods of the algorithms. The security analysis and experimental results show that MTA–PA is a secure and efficient data protection scheme in smart grid.

Efficient and Privacy-Preserving Data Aggregation Scheme for Smart Grid Against Internal Adversaries

Privacy-preserving data aggregation (P2DA) is an important basic building block that can protect consumer’s privacy in the smart grid environment because it could be used to prevent the extraction of the electricity consumption information of a specific consumer. Due to this important function, the P2DA scheme for the smart grid has attracted a lot of attention from both academic and industry researchers who have proposed many P2DA schemes for the smart grid in recent years. However, most of these P2DA schemes are not secure against internal attackers or cannot provide data integrity. Besides, their computation costs are not satisfactory because the bilinear pairing operation or the hash-to-point operation is performed at the smart meter’s side. To address the deficiencies of previous schemes, we propose a new P2DA scheme against internal attackers using Boneh–Goh–Nissim public key cryptography. The proposed P2DA scheme does not use bilinear pairing or hash-to-point operation making it be more computationally efficient than previous P2DA schemes. We also show that the proposed P2DA scheme is provably secure and can meet various security requirements.

A New Differentially Private Data Aggregation With Fault Tolerance for Smart Grid Communications

Privacy-preserving data aggregation has been widely studied to meet the requirement of timely monitoring measurements of users while protecting individual’s privacy in smart grid communications. In this paper, a new secure data aggregation scheme, named d ifferentially p rivate data a ggregation with f ault t olerance (DPAFT), is proposed, which can achieve differential privacy and fault tolerance simultaneously. Specifically, inspired by the idea of Diffie–Hellman key exchange protocol, an artful constraint relation is constructed for data aggregation. With this novel constraint, DPAFT can support fault tolerance of malfunctioning smart meters efficiently and flexibly. In addition, DPAFT is also enhanced to resist against differential attacks, which are suffered in most of the existing data aggregation schemes. By improving the basic Boneh–Goh–Nissim cryptosystem to be more applicable to the practical scenarios, DPAFT can resist much stronger adversaries, i.e., user’s privacy can be protected in the honest-but-curious model. Extensive performance evaluations are further conducted to illustrate that DPAFT outperforms the state-of-the-art data aggregation schemes in terms of storage cost, computation complexity, utility of differential privacy, robustness of fault tolerance, and the efficiency of user addition and removal.

PPGJ: A privacy‐preserving general join for outsourced encrypted database

ABSTRACTIn outsourced database, it is desirable to store sensitive data in an encrypted form to reduce security and privacy risks because the server may not be fully trusted. Several approaches have been proposed in recent literatures to efficiently support queries on encrypted databases. Most researches focus on keywords search and range query while few works study on join query because of a lack of related cryptography primitives. We propose a solution of a privacy‐preserving general join supporting both equality tests and simple non‐equality tests on ciphertexts by using the revised Boneh–Goh–Nissim encryption algorithm and a Bloom filter. Finally, we analyze its advantages and disadvantages by the comparison with existing method on the performance and the newly introduced security notions. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Efficient and secure protocols for privacy-preserving set operations

Many applications require performing set operations without publishing individual datesets. In this article, we address this problem for five fundamental set operations including set intersection, cardinality of set intersection, element reduction, overthreshold set-union, and subset relation. Our protocols are obtained in the universally composable security framework, in the assumption of the probabilistic polynomial time bounded adversary, which actively controls a fixed set of t parties and the assumption of an authenticated broadcast channel. Our constructions utilize building blocks of nonmalleable NonInteractive Zero-Knowledge (NIZK) arguments, which are based on a ( t + 1, N )-threshold version ( N is the number of parties in the protocol) of the boneh-goh-nissim (BGN) cryptosystem whose underlying group supports bilinear maps, in the assumption that the public key and shares of the secret key have been generated by a trusted dealer. The previous studies were all based on the stand-alone model with the same assumptions on the adversary, broadcast channel, and key generation. For the first four operations, we propose protocols that improve the previously known results by an O ( N ) factor in the computation and communication complexities. For the subset relation, our protocol is the first one secure against the active adversary. Our constructions of NIZK have independent interest in that, though also mentioned as building blocks, the previous work did not illustrate how to construct them. We construct these NIZK with an additional nonmalleable property, the same complexity as claimed in the previous work, and also an improvement on the communication complexity.