Multi-Input Attribute Based Encryption and Predicate Encryption

Motivated by several new and natural applications, we initiate the study of multi-input predicate encryption (${\sf miPE}$) and further develop multi-input attribute based encryption (${\sf miABE}$). Our contributions are: 1. Formalizing Security: We provide definitions for ${\sf miABE}$ and ${\sf miPE}$ in the {symmetric} key setting and formalize security in the standard indistinguishability (IND) paradigm, against unbounded collusions. 2. Two-input ${\sf ABE}$ for ${\sf NC}_1$ from ${\sf LWE}$ and Pairings: We provide the first constructions for two-input key-policy ${\sf ABE}$ for ${\sf NC}_1$ from ${\sf LWE}$ and pairings. Our construction leverages a surprising connection between techniques recently developed by Agrawal and Yamada (Eurocrypt, 2020) in the context of succinct single-input ciphertext-policy ${\sf ABE}$, to the seemingly unrelated problem of two-input key-policy ${\sf ABE}$. Similarly to Agrawal-Yamada, our construction is proven secure in the bilinear generic group model. By leveraging inner product functional encryption and using (a variant of) the KOALA knowledge assumption, we obtain a construction in the standard model analogously to Agrawal, Wichs and Yamada (TCC, 2020). 3. Heuristic two-input ${\sf ABE}$ for ${\sf P}$ from Lattices: We show that techniques developed for succinct single-input ciphertext-policy ${\sf ABE}$ by Brakerski and Vaikuntanathan (ITCS 2022) can also be seen from the lens of ${\sf miABE}$ and obtain the first two-input key-policy ${\sf ABE}$ from lattices for ${\sf P}$. 4. Heuristic three-input ${\sf ABE}$ and ${\sf PE}$ for ${\sf NC}_1$ from Pairings and Lattices: We obtain the first three-input ${\sf ABE}$ for ${\sf NC}_1$ by harnessing the powers of both the Agrawal-Yamada and the Brakerski-Vaikuntanathan constructions. 5. Multi-input ${\sf ABE}$ to multi-input ${\sf PE}$ via Lockable Obfuscation: We provide a generic compiler that lifts multi-input ${\sf ABE}$ to multi-input ${\sf PE}$ by relying on the hiding properties of Lockable Obfuscation (${\sf LO}$) by Wichs-Zirdelis and Goyal-Koppula-Waters (FOCS 2018), which can be based on ${\sf LWE}$. Our compiler generalizes such a compiler for single-input setting to the much more challenging setting of multiple inputs. By instantiating our compiler with our new two and three-input ${\sf ABE}$ schemes, we obtain the first constructions of two and three-input ${\sf PE}$ schemes. Our constructions of multi-input ${\sf ABE}$ provide the first improvement to the compression factor of non-trivially exponentially efficient Witness Encryption defined by Brakerski et al. (SCN 2018) without relying on compact functional encryption or indistinguishability obfuscation. We believe that the unexpected connection between succinct single-input ciphertext-policy ${\sf ABE}$ and multi-input key-policy ${\sf ABE}$ may lead to a new pathway for witness encryption

Ad Hoc Multi-Input Functional Encryption

Consider sources that supply sensitive data to an aggregator. Standard encryption only hides the data from eavesdroppers, but using specialized encryption one can hope to hide the data (to the extent possible) from the aggregator itself. For flexibility and security, we envision schemes that allow sources to supply encrypted data, such that at any point a dynamically-chosen subset of sources can allow an agreed-upon joint function of their data to be computed by the aggregator. A primitive called multi-input functional encryption (MIFE), due to Goldwasser et al. (EUROCRYPT 2014), comes close, but has two main limitations: - it requires trust in a third party, who is able to decrypt all the data, and - it requires function arity to be fixed at setup time and to be equal to the number of parties. To drop these limitations, we introduce a new notion of ad hoc MIFE. In our setting, each source generates its own public key and issues individual, function-specific secret keys to an aggregator. For successful decryption, an aggregator must obtain a separate key from each source whose ciphertext is being computed upon. The aggregator could obtain multiple such secret-keys from a user corresponding to functions of varying arity. For this primitive, we obtain the following results: - We show that standard MIFE for general functions can be bootstrapped to ad hoc MIFE for free, i.e. without making any additional assumption. - We provide a direct construction of ad hoc MIFE for the inner product functionality based on the Learning with Errors (LWE) assumption. This yields the first construction of this natural primitive based on a standard assumption. At a technical level, our results are obtained by combining standard MIFE schemes and two-round secure multiparty computation (MPC) protocols in novel ways highlighting an interesting interplay between MIFE and two-round MPC

Decentralizing Inner-Product Functional Encryption

International audienceMulti-client functional encryption (MCFE) is a more flexible variant of functional encryption whose functional decryption involves multiple ciphertexts from different parties. Each party holds a different secret key and can independently and adaptively be corrupted by the adversary. We present two compilers for MCFE schemes for the inner-product functionality, both of which support encryption labels. Our first compiler transforms any scheme with a special key-derivation property into a decentralized scheme, as defined by Chotard et al. (ASIACRYPT 2018), thus allowing for a simple distributed way of generating functional decryption keys without a trusted party. Our second compiler allows to lift an unnatural restriction present in existing (decentralized) MCFE schemes, which requires the adversary to ask for a ciphertext from each party. We apply our compilers to the works of Abdalla et al. (CRYPTO 2018) and Chotard et al. (ASIACRYPT 2018) to obtain schemes with hitherto unachieved properties. From Abdalla et al., we obtain instantiations of DMCFE schemes in the standard model (from DDH, Paillier, or LWE) but without labels. From Chotard et al., we obtain a DMCFE scheme with labels still in the random oracle model, but without pairings

Unbounded Predicate Inner Product Functional Encryption from Pairings

Predicate inner product functional encryption (P-IPFE) is essentially attribute-based IPFE (AB-IPFE) which additionally hides attributes associated to ciphertexts. In a P-IPFE, a message x is encrypted under an attribute w and a secret key is generated for a pair (y, v) such that recovery of ⟨ x, y⟩ requires the vectors w, v to satisfy a linear relation. We call a P-IPFE unbounded if it can encrypt unbounded length attributes and message vectors. ∙ zero predicate IPFE. We construct the first unbounded zero predicate IPFE (UZP-IPFE) which recovers ⟨ x, y⟩ if ⟨ w, v⟩ = 0 . This construction is inspired by the unbounded IPFE of Tomida and Takashima (ASIACRYPT 2018) and the unbounded zero inner product encryption of Okamoto and Takashima (ASIACRYPT 2012). The UZP-IPFE stands secure against general attackers capable of decrypting the challenge ciphertext. Concretely, it provides full attribute-hiding security in the indistinguishability-based semi-adaptive model under the standard symmetric external Diffie–Hellman assumption. ∙ non-zero predicate IPFE. We present the first unbounded non-zero predicate IPFE (UNP-IPFE) that successfully recovers ⟨ x, y⟩ if ⟨ w, v⟩ ≠ 0 . We generically transform an unbounded quadratic FE (UQFE) scheme to weak attribute-hiding UNP-IPFE in both public and secret key setting. Interestingly, our secret key simulation secure UNP-IPFE has succinct secret keys and is constructed from a novel succinct UQFE that we build in the random oracle model. We leave the problem of constructing a succinct public key UNP-IPFE or UQFE in the standard model as an important open problem

Still Wrong Use of Pairings in Cryptography

Several pairing-based cryptographic protocols are recently proposed with a wide variety of new novel applications including the ones in emerging technologies like cloud computing, internet of things (IoT), e-health systems and wearable technologies. There have been however a wide range of incorrect use of these primitives. The paper of Galbraith, Paterson, and Smart (2006) pointed out most of the issues related to the incorrect use of pairing-based cryptography. However, we noticed that some recently proposed applications still do not use these primitives correctly. This leads to unrealizable, insecure or too inefficient designs of pairing-based protocols. We observed that one reason is not being aware of the recent advancements on solving the discrete logarithm problems in some groups. The main purpose of this article is to give an understandable, informative, and the most up-to-date criteria for the correct use of pairing-based cryptography. We thereby deliberately avoid most of the technical details and rather give special emphasis on the importance of the correct use of bilinear maps by realizing secure cryptographic protocols. We list a collection of some recent papers having wrong security assumptions or realizability/efficiency issues. Finally, we give a compact and an up-to-date recipe of the correct use of pairings.Comment: 25 page

Class Based Multi Stage Encryption for Efficient Data Security in Cloud Environment Using Profile Data

The security issues in the cloud have been well studied. The data security has much importance in point of data owner. There are number of approaches presented earlier towards performance in data security in cloud. To overcome the issues, a class based multi stage encryption algorithm is presented in this paper. The method classifies the data into number of classes and different encryption scheme is used for different classes in different levels. Similarly, the user has been authenticated for their access and they have been classified into different categories. According to the user profile, the method restricts the access of user and based on the same, the method defines security measures. A system defined encryption methodology is used for encrypting the data. Moreover, the user has been returned with other encryption methods which can be decrypted by the user using their own key provided by the system. The proposed algorithm improves the performance of security and improves the data security

A Programmable SoC-Based Accelerator for Privacy-Enhancing Technologies and Functional Encryption

A multitude of privacy-enhancing technologies (PETs) has been presented recently to solve the privacy problems of contemporary services utilizing cloud computing. Many of them are based on additively homomorphic encryption (AHE) that allows the computation of additions on encrypted data. The main technical obstacles for adaptation of PETs in practical systems are related to performance overheads compared with current privacy-violating alternatives. In this article, we present a hardware/software (HW/SW) codesign for programmable systems-on-chip (SoCs) that is designed for accelerating applications based on the Paillier encryption. Our implementation is a microcode-based multicore architecture that is suitable for accelerating various PETs using AHE with large integer modular arithmetic. We instantiate the implementation in a Xilinx Zynq-7000 programmable SoC and provide performance evaluations in real hardware. We also investigate its efficiency in a high-end Xilinx UltraScale+ programmable SoC. We evaluate the implementation with two target use cases that have relevance in PETs: privacy-preserving computation of squared Euclidean distances over encrypted data and multi-input functional encryption (FE) for inner products. Both of them represent the first hardware acceleration results for such operations, and in particular, the latter one is among the very first published implementation results of FE on any platform.Peer reviewe

Speed reading in the dark : Accelerating functional encryption for quadratic functions with reprogrammable hardware

Functional encryption is a new paradigm for encryption where decryption does not give the entire plaintext but only some function of it. Functional encryption has great potential in privacy-enhancing technologies but suffers from excessive computational overheads. We introduce the first hardware accelerator that supports functional encryption for quadratic functions. Our accelerator is implemented on a reprogrammable system-on-chip following the hardware/software codesign methogol-ogy. We benchmark our implementation for two privacy-preserving machine learning applications: (1) classification of handwritten digits from the MNIST database and (2) classification of clothes images from the Fashion MNIST database. In both cases, classification is performed with encrypted images. We show that our implementation offers speedups of over 200 times compared to a published software implementation and permits applications which are unfeasible with software-only solutions.Peer reviewe