Hybrid Encryption in the Multi-User Setting

This paper presents an attack in the multi-user setting on various public-key encryption schemes standardized in IEEE 1363a, SECG SEC 1 and ISO 18033-2. The multi-user setting is a security model proposed by Bellare et al., which allows adversaries to simultaneously attack multiple ciphertexts created by one or more users. An attack is considered successful if the attacker learns information about any of the plaintexts. We show that many standardized public-key encryption schemes are vulnerable in this model, and give ways to prevent the attack. We also show that the key derivation function and pseudorandom generator used to implement a hybrid encryption scheme must be secure in the multi-user setting, in order for the overall primitive to be secure in the multi-user setting. As an illustration of the former, we show that using HKDF (as standardized in NIST SP 800-56C) as a key derivation function for certain standardized hybrid public-key encryption schemes is insecure in the multi-user setting

Shared and searchable encrypted data for untrusted servers

Current security mechanisms are not suitable for organisations that outsource their data management to untrusted servers. Encrypting and decrypting sensitive data at the client side is the normal approach in this situation but has high communication and computation overheads if only a subset of the data is required, for example, selecting records in a database table based on a keyword search. New cryptographic schemes have been proposed that support encrypted queries over encrypted data. But they all depend on a single set of secret keys, which implies single user access or sharing keys among multiple users, with key revocation requiring costly data re-encryption. In this paper, we propose an encryption scheme where each authorised user in the system has his own keys to encrypt and decrypt data. The scheme supports keyword search which enables the server to return only the encrypted data that satisfies an encrypted query without decrypting it. We provide a concrete construction of the scheme and give formal proofs of its security. We also report on the results of our implementation

Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

Public-Key Encryption with Delegated Search

In public-key setting, Alice encrypts email with public key of Bob, so that only Bob will be able to learn contents of email. Consider scenario when computer of Alice is infected and unbeknown to Alice it also embeds malware into message. Bob's company, Carol, cannot scan his email for malicious content as it is encrypted so burden is on Bob to do scan. This is not efficient. We construct mechanism that enables Bob to provide trapdoors to Carol such that Carol, given encrypted data and malware signature, is able to check whether encrypted data contains malware signature, without decrypting it. We refer to this mechanism as Public-Key Encryption with Delegated Search SPKE.\ud \ud We formalize SPKE and give construction based on ElGamal public-key encryption (PKE). proposed scheme has ciphertexts which are both searchable and decryptable. This property of scheme is crucial since entity can search entire content of message, in contrast to existing searchable public-key encryption schemes where search is done only in metadata part. We prove in standard model that scheme is ciphertext indistinguishable and trapdoor indistinguishable under Symmetric External Diffie-Hellman (sxdh) assumption. We prove also ciphertext one-wayness of scheme under modified Computational Diffie-Hellman (mcdh) assumption. We show that our PKEDS scheme can be used in different applications such as detecting encrypted malwares and forwarding encrypted emails