Searchable Encryption with Access Control

Outsourcing data to the cloud is becoming increasingly prevalent. To ensure data confidentiality, encrypting the data before outsourcing it is advised. While encryption protects the secrets in the data, it also prevents operations on the data. For example in a multi-user setting, data is often accessed via search, but encryption prevents search. Searchable encryption solves this dilemma. However, in a multi-user setting not all users may be allowed to access all data, requiring some means of access control. We address the question how searchable encryption and access control can be combined. Combining these technologies is required to achieve strong notions of confidentiality: if a ciphertext occurs as a search result, we learn something about the underlying document, even if access control does not let us access the document. This illustrates a need to link search and access control, so that search results presented to users only feature data the users are allowed to access. Our searchable encryption scheme with access control establishes that link

Why Your Encrypted Database Is Not Secure

Encrypted databases, a popular approach to protecting data from compromised database management systems (DBMS’s), use abstract threat models that capture neither realistic databases, nor realistic attack scenarios. In particular, the “snapshot attacker” model used to support the security claims for many encrypted databases does not reflect the information about past queries available in any snapshot attack on an actual DBMS. We demonstrate how this gap between theory and reality causes encrypted databases to fail to achieve their “provable security” guarantees

Analogy-Making as a Core Primitive in the Software Engineering Toolbox

An analogy is an identification of structural similarities and correspondences between two objects. Computational models of analogy making have been studied extensively in the field of cognitive science to better understand high-level human cognition. For instance, Melanie Mitchell and Douglas Hofstadter sought to better understand high-level perception by developing the Copycat algorithm for completing analogies between letter sequences. In this paper, we argue that analogy making should be seen as a core primitive in software engineering. We motivate this argument by showing how complex software engineering problems such as program understanding and source-code transformation learning can be reduced to an instance of the analogy-making problem. We demonstrate this idea using Sifter, a new analogy-making algorithm suitable for software engineering applications that adapts and extends ideas from Copycat. In particular, Sifter reduces analogy-making to searching for a sequence of update rule applications. Sifter uses a novel representation for mathematical structures capable of effectively representing the wide variety of information embedded in software. We conclude by listing major areas of future work for Sifter and analogy-making in software engineering.Comment: Conference paper at SPLASH 'Onward!' 2020. Code is available at https://github.com/95616ARG/sifte

Homomorphic string search with constant multiplicative depth

String search finds occurrences of patterns in a larger text. This general problem occurs in various application scenarios, f.e. Internet search, text processing, DNA analysis, etc. Using somewhat homomorphic encryption with SIMD packing, we provide an efficient string search protocol that allows to perform a private search in outsourced data with minimal preprocessing. At the base of the string search protocol lies a randomized homomorphic equality circuit whose depth is independent of the pattern length. This circuit not only improves the performance but also increases the practicality of our protocol as it requires the same set of encryption parameters for a wide range of patterns of different lengths. This constant depth algorithm is about 10 times faster than the prior work. It takes about 5 minutes on an average laptop to find the positions of a string with at most 50 UTF-32 characters in a text with 1000 characters. In addition, we provide a method that compresses the search results, thus reducing the communication cost of the protocol. For example, the communication complexity for searching a string with 50 characters in a text of length 10000 is about 347 KB and 13.9 MB for a text with 1000000 characters

Practical Forward Secure Group Signature Schemes

A group signature scheme allows a group member to sign messages anonymously on behalf of the group, while in case of a dispute, a designated entity can reveal the identity of a signature’s originator. Group signature schemes can be used as a basic building block for many security applications such as electronic banking systems and electronic voting. Two important issues – forward security and efficient revocation – have not been addressed by prior schemes. We construct the first forward-secure group signature schemes. While satisfying all the security properties proposed in previous group signature schemes, our schemes provide a new desired security property, forward-security: while the group public key stays fixed, a group signing key of a group member evolves over time such that compromise of a group signing key of the current time period does not enable an attacker to forge group signatures pertaining to the past time periods. Such forward-security is important to mitigate the damage caused by key exposure and particularly desirable for group signature schemes because the risk of signing key exposure escalates as the size of the group increases. Our schemes are provably secure in the random oracle model and under the strong RSA and decisional Diffie Hellman assumptions. Furthermore, we extend our forward-secure group signature scheme to provide a solution for the problem of group member exclusion without the need to re-key all other group members. When a group member is excluded, he should not be able to generate valid signatures any more and yet ∗ We gratefully acknowledge funding support for this research