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

DIPSAUCE: Efficient Private Stream Aggregation Without Trusted Parties

Private Stream Aggregation (PSA) schemes are efficient protocols for distributed data analytics. In a PSA scheme, a set of data producers can encrypt data for a central party so that it learns the sum of all (encrypted) values, but nothing about each individual value. Due to this ability to efficiently enable central data analytics without leaking individual user data, PSA schemes are often used for IoT data analytics scenarios where privacy is important, such as smart metering. However, all known PSA schemes require a trusted party for key generation, which is undesirable from a privacy standpoint. Further, even though the main benefit of PSA schemes over alternative technologies such as Functional Encryption is that they are efficient enough to run on IoT devices, there exists no evaluation of the efficiency of existing PSA schemes on realistic IoT devices. In this paper, we address both these issues. We first evaluate the efficiency of the state of the art PSA schemes on realistic IoT devices. We then propose, implement and evaluate a DIstributed setup PSA scheme for Use in Constrained Environments (DIPSAUCE). DIPSAUCE is the first PSA scheme that does not rely on a trusted party. Our security and efficiency evaluation shows that it is indeed possible to construct an efficient PSA scheme without a trusted central party. Surprisingly, our results also show that, a side effect, our method for distributing the setup procedure also makes the encryption procedure more efficient than the state of the art PSA schemes which rely on trusted parties

Agora: A Privacy-Aware Data Marketplace

We propose Agora, the first blockchain-based data marketplace that enables multiple privacy-concerned parties to get compensated for contributing and exchanging data, without relying on a trusted third party during the exchange. Agora achieves data privacy, output verifiability, and atomicity of payments by leveraging cryptographic techniques, and is designed as a decentralized application via smart contracts. Particularly, data generators provide encrypted data to data brokers who use a functional secret key to learn nothing but the output of a specific, agreed upon, function over the raw data. Data consumers can purchase decrypted outputs from the brokers, accompanied by corresponding proofs of correctness. We implement a working prototype of Agora on Ethereum and experimentally evaluate its performance and deployment costs. As a core building block of Agora, we propose a new functional encryption scheme with additional public parameters that operate as a trust anchor for verifying decrypted results

cuFE: High Performance Privacy Preserving Support Vector Machine with Inner-Product Functional Encryption

Privacy preservation is a sensitive issue in our modern society. It is becoming increasingly important in many applications in this ever-growing and highly connected digital era. Functional encryption is a computation on encrypted data paradigm that allows users to retrieve the evaluation of a function on encrypted data without revealing the data, thus effectively protecting users\u27 privacy. However, existing functional encryption implementations are still very time-consuming for practical deployment, especially when applied to machine learning applications that involve a huge amount of data. In this paper, we present a high-performance implementation of inner-product functional encryption (IPFE) based on ring-learning with errors on graphics processing units. We propose novel techniques to parallelize the Gaussian sampling, which is one of the most time-consuming operations in the IPFE scheme. We further execute a systematic investigation to select the best strategy for implementing number theoretic transform and inverse number theoretic transform for different security levels. Compared to the existing AVX2 implementation of IPFE, our implementation on a RTX 2060 GPU device can achieve 34.24x, 40.02x, 156.30x, and 18.76x speed-up for Setup, Encrypt, KeyGen, and Decrypt respectively. Finally, we propose a fast privacy-preserving Support Vector Machine (SVM) application to classify data securely using our GPU-accelerated IPFE scheme. Experimental results show that our implementation can classify 100 inputs with 591 support vectors in 688 ms (less than a second), which is 33.12x faster than the AVX2 version which takes 23 seconds

Lower Bounds for Lattice-based Compact Functional Encryption

Functional encryption (FE) is a primitive where the holder of a master secret key can control which functions a user can evaluate on encrypted data. It is a powerful primitive that even implies indistinguishability obfuscation (iO), given sufficiently compact ciphertexts (Ananth-Jain, CRYPTO\u2715 and Bitansky-Vaikuntanathan, FOCS\u2715). However, despite being extensively studied, there are FE schemes, such as function-hiding inner-product FE (Bishop-Jain-Kowalczyk, AC\u2715, Abdalla-Catalano-Fiore-Gay-Ursu, CRYPTO’18) and compact quadratic FE (Baltico-Catalano-Fiore-Gay, Lin, CRYPTO’17), that can be only realized using pairings. This raises whether there are some mathematical barriers which hinder us from realizing these FE schemes from other assumptions. In this paper, we study the difficulty of constructing lattice-based compact FE. We generalize the impossibility results of Ünal (EC\u2720) for lattice-based function-hiding FE, and extend it to the case of compact FE. Concretely, we prove lower bounds for lattice-based compact FE schemes which meet some (natural) algebraic restrictions at encryption and decryption, and have messages and ciphertexts of constant dimensions. We see our results as important indications of why it is hard to construct lattice-based FE schemes for new functionalities, and which mathematical barriers have to be overcome

(Inner-Product) Functional Encryption with Updatable Ciphertexts

We propose a novel variant of functional encryption which supports ciphertext updates, dubbed ciphertext-updatable functional encryption (CUFE). Such a feature further broadens the practical applicability of the functional-encryption paradigm and allows for fine-grained access control even after a ciphertext is generated. Updating ciphertexts is carried out via so-called update tokens which a dedicated party can use to convert ciphertexts. However, allowing update tokens requires some care for the security definition. Our contribution is three-fold: a) We define our new primitive with a security notion in the indistinguishability setting. Within CUFE, functional decryption keys and ciphertexts are labeled with tags such that only if the tags of the decryption key and the ciphertext match, then decryption succeeds. Furthermore, we allow ciphertexts to switch their tags to any other tag via update tokens. Such tokens are generated by the holder of the main secret key and can only be used in the desired direction. b) We present a generic construction of CUFE for any functionality as well as predicates different from equality testing on tags which relies on the existence of indistinguishability obfuscation (iO). c) We present a practical construction of CUFE for the inner-product functionality from standard assumptions (i.e., LWE) in the random-oracle model. On the technical level, we build on the recent functional-encryption schemes with fine-grained access control and linear operations on encrypted data (Abdalla et al., AC\u2720) and introduce an additional ciphertext-updatability feature. Proving security for such a construction turned out to be non-trivial, particularly when revealing keys for the updated challenge ciphertext is allowed. Overall, such construction enriches the set of known inner-product functional-encryption schemes with the additional updatability feature of ciphertexts

Functional Encryption for Attribute-Weighted Sums from k-Lin

International audienceWe present functional encryption schemes for attribute-weighted sums, where encryption takes as input N attribute-value pairs (x i , z i) where x i is public and z i is private; secret keys are associated with arithmetic branching programs f , and decryption returns the weighted sum N i =1 f (x i)z i while leaking no additional information about the z i 's. Our main construction achieves (1) compact public parameters and key sizes that are independent of N and the secret key can decrypt a ciphertext for any a-priori unbounded N ; (2) short ciphertexts that grow with N and the size of z i but not x i ; (3) simulation-based security against unbounded collusions; (4) relies on the standard k-linear assumption in prime-order bilinear groups

Dynamic Decentralized Functional Encryption

International audienceWe introduce Dynamic Decentralized Functional Encryption (DDFE), a generalization ofFunctional Encryption which allows multiple users to join the system dynamically, without relying on atrusted third party or on expensive and interactive Multi-Party Computation protocols.This notion subsumes existing multi-user extensions of Functional Encryption, such as Multi-Input, Multi-Client, and Ad Hoc Multi-Input Functional Encryption.We define and construct schemes for various functionalities which serve as building-blocks for latter primitivesand may be useful in their own right, such as a scheme for dynamically computing sums in any Abeliangroup. These constructions build upon simple primitives in a modular way, and have instantiations fromwell-studied assumptions, such as DDH or LWE.Our constructions culminate in an Inner-Product scheme for computing weighted sums on aggregatedencrypted data, from standard assumptions in prime-order groups in the Random Oracle Model

Registered Functional Encryption for Quadratic Functions from MDDH

We present a Registered Functional Encryption (RFE) scheme for inner product and a RFE scheme for quadratic functions based on pairings and relying on the Matrix Decision Diffie-Hellman (MDDH) assumption and bilateral MDDH assumption. Previously, RFE is only known to be constructed from indistinguishability obfuscation (iO) in Francati-Friolo-Maitra-Malavolta-Rahimi-Venturi [Asiacrypt \u2723]