Identity-Based Functional Encryption for Quadratic Functions from Lattices

2018 ◽  
pp. 409-425 ◽  
Author(s):  
Kelly Yun ◽  
Xin Wang ◽  
Rui Xue
Keyword(s):  
Quadratic Functions ◽  
Functional Encryption ◽  
Identity Based

scholarly journals Controlled Functional Encryption Revisited: Multi-Authority Extensions and Efficient Schemes for Quadratic Functions

2021 ◽  
Vol 2021 (1) ◽  
pp. 21-42
Author(s):  
Miguel Ambrona ◽  
Dario Fiore ◽  
Claudio Soriente
Keyword(s):  
Homomorphic Encryption ◽  
Linear Functions ◽  
Quadratic Functions ◽  
Functional Encryption ◽  
Fine Grained ◽  
Multiple Data ◽  
Garbled Circuits ◽  
Private Data ◽  
Data Owner ◽  
Single Data

AbstractIn a Functional Encryption scheme (FE), a trusted authority enables designated parties to compute specific functions over encrypted data. As such, FE promises to break the tension between industrial interest in the potential of data mining and user concerns around the use of private data. FE allows the authority to decide who can compute and what can be computed, but it does not allow the authority to control which ciphertexts can be mined. This issue was recently addressed by Naveed et al., that introduced so-called Controlled Functional encryption (or C-FE), a cryptographic framework that extends FE and allows the authority to exert fine-grained control on the ciphertexts being mined. In this work we extend C-FE in several directions. First, we distribute the role of (and the trust in) the authority across several parties by defining multi-authority C-FE (or mCFE). Next, we provide an efficient instantiation that enables computation of quadratic functions on inputs provided by multiple data-owners, whereas previous work only provides an instantiation for linear functions over data supplied by a single data-owner and resorts to garbled circuits for more complex functions. Our scheme leverages CCA2 encryption and linearly-homomorphic encryption. We also implement a prototype and use it to showcase the potential of our instantiation.

scholarly journals Speed Reading in the Dark: Accelerating Functional Encryption for Quadratic Functions with Reprogrammable Hardware

2021 ◽  
pp. 1-27
Author(s):  
Milad Bahadori ◽  
Kimmo Järvinen ◽  
Tilen Marc ◽  
Miha Stopar
Keyword(s):  
Quadratic Functions ◽  
Hardware Accelerator ◽  
Functional Encryption ◽  
New Paradigm ◽  
Speed Reading ◽  
Machine Learning Applications ◽  
Privacy Enhancing Technologies ◽  
On Chip ◽  
Encrypted Images

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 methogology. 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.

Hierarchical Identity-based Inner Product Functional Encryption

2021 ◽  
Author(s):  
Ge Song ◽  
Yuqiao Deng ◽  
Qiong Huang ◽  
Changgen Peng ◽  
Chunming Tang ◽  
...  
Keyword(s):  
Inner Product ◽  
Functional Encryption ◽  
Identity Based
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