Function-Hiding Inner Product Encryption is Practical

In a functional encryption scheme, secret keys are associated with functions and ciphertexts are associated with messages. Given a secret key for a function f, and a ciphertext for a message x, a decryptor learns f(x) and nothing else about x. Inner product encryption is a special case of functional encryption where both secret keys and ciphertext are associated with vectors. The combination of a secret key for a vector x and a ciphertext for a vector y reveal and nothing more about y. An inner product encryption scheme is function- hiding if the keys and ciphertexts reveal no additional information about both x and y beyond their inner product. In the last few years, there has been a flurry of works on the construction of function-hiding inner product encryption, starting with the work of Bishop, Jain, and Kowalczyk (Asiacrypt 2015) to the more recent work of Tomida, Abe, and Okamoto (ISC 2016). In this work, we focus on the practical applications of this primitive. First, we show that the parameter sizes and the run-time complexity of the state-of-the-art construction can be further reduced by another factor of 2, though we compromise by proving security in the generic group model. We then show that function privacy enables a number of applications in biometric authentication, nearest-neighbor search on encrypted data, and single-key two-input functional encryption for functions over small message spaces. Finally, we evaluate the practicality of our encryption scheme by implementing our function-hiding inner product encryption scheme. Using our construction, encryption and decryption operations for vectors of length 50 complete in a tenth of a second in a standard desktop environment

Multi Random Projection Inner Product Encryption, Applications to Proximity Searchable Encryption for the Iris Biometric

Biometric databases collect people’s information and allow users to perform proximity searches (finding all records within a bounded distance of the query point) with few cryptographic protections. This work studies proximity searchable encryption applied to the iris biometric. Prior work proposed inner product functional encryption as a technique to build proximity biometric databases (Kim et al., SCN 2018). This is because binary Hamming distance is computable using an inner product. This work identifies and closes two gaps in using inner product encryption for biometric search: 1. Biometrics naturally use long vectors often with thousands of bits. Many inner product encryption schemes generate a random matrix whose dimension scales with vector size and have to invert this matrix. As a result, setup is not feasible on commodity hardware unless we reduce the dimension of the vectors. We explore state-of-the-art techniques to reduce the dimension of the iris biometric and show that all known techniques harm the accuracy of the resulting system. That is, for small vector sizes multiple unrelated biometrics are returned in the search. For length 64 vectors, at a 90% probability of the searched biometric being returned, 10% of stored records are erroneously returned on average. Rather than changing the feature extractor, we introduce a new cryptographic technique that allows one to generate several smaller matrices. For vectors of length 1024 this reduces the time to run setup from 23 days to 4 minutes. At this vector length, for the same 90% probability of the searched biometric being returned, .02% of stored records are erroneously returned on average. 2. Prior inner product approaches leak distance between the query and all stored records. We refer to these as distance-revealing. We show a natural construction from function hiding, secret-key, predicate, inner product encryption (Shen, Shi, and Waters, TCC 2009). Our construction only leaks access patterns and which returned records are the same distance from the query. We refer to this scheme as distance-hiding. We implement and benchmark one distance-revealing and one distance-hiding scheme. The distance-revealing scheme can search a small (hundreds) database in 4 minutes while the distance-hiding scheme is not yet practical, requiring 3.5 hours. As a technical contribution of independent interest, we show that our scheme can be instantiated using symmetric pairing groups reducing the cost of search by roughly a factor of three. We believe this analysis extends to other schemes based on projections to a random linear map and its inverse analyzed in the generic group model

Blind Bernoulli Trials: A Noninteractive Protocol for Hidden-Weight Coin Flips

We introduce the concept of a Blind Bernoulli Trial, a noninteractive protocol that allows a set of remote, disconnected users to individually compute one random bit each with probability p defined by the sender, such that no receiver learns any more information about p than strictly necessary. We motivate the problem by discussing several possible applications in secure distributed systems. We then formally define the problem in terms of correctness and security definitions and explore possible solutions using existing cryptographic primitives. We prove the security of an efficient solution in the standard model. Finally, we implement the solution and give performance results that show it is practical with current hardware

On the Key-Uncertainty of Quantum Ciphers and the Computational Security of One-way Quantum Transmission

We consider the scenario where Alice wants to send a secret (classical) $n$-bit message to Bob using a classical key, and where only one-way transmission from Alice to Bob is possible. In this case, quantum communication cannot help to obtain perfect secrecy with key length smaller then $n$. We study the question of whether there might still be fundamental differences between the case where quantum as opposed to classical communication is used. In this direction, we show that there exist ciphers with perfect security producing quantum ciphertext where, even if an adversary knows the plaintext and applies an optimal measurement on the ciphertext, his Shannon uncertainty about the key used is almost maximal. This is in contrast to the classical case where the adversary always learns $n$ bits of information on the key in a known plaintext attack. We also show that there is a limit to how different the classical and quantum cases can be: the most probable key, given matching plain- and ciphertexts, has the same probability in both the quantum and the classical cases. We suggest an application of our results in the case where only a short secret key is available and the message is much longer.Comment: 19 pages, 2 figures. This is a revised version of an earlier version that appeared in the proc. of Eucrocrypt'04:LNCS3027, 200