Searchable encryption allows users to perform search operations on encrypted data before decrypting it first. Secret sharing is one of the most important cryptographic primitives used to design an information theoretic scheme. Nowadays cryptosys-tem designers are providing a facility to adjust the security parameters in real time to circumvent AI-enabled cyber security threats. For long term security of data which is used by various applications, proactive secret sharing allows the shares of the original secret to be dynamically adjusted during a specific interval of time. In proactive secret sharing, the updation of shares at regular intervals of time is done by the servers (participants) and not by the dealer. In this paper, we propose a novel proactive secret sharing scheme where the shares stored at servers are updated using preshared pairwise keys between servers at regular intervals of time. The direct search of words over sentences using the conjunctive search function without the generation of any index is possible using the underlying querying method.

Symmetric searchable encryption (SSE) schemes are preferred over asymmetric ones for their lower computational cost. Owing to the big data size of most of the cloud applications, SSE with keyword search often yields a large number of search results matching the search criterion, but only a small portion of them is of actual interest. This results in unnecessary increase of network traffic. A customized search against a phrase instead of keywords can yield more specific and relevant search results and can reduce the network traffic. This motivates the idea of phrase search in SSE. Most of the existing symmetric key searchable encryption schemes either do not support phrase search or have unwanted leakage associated with them. In this paper, we propose a symmetric key searchable encryption scheme for phrase search that minimizes the leakage of information from search pattern and access pattern. We propose a probabilistic trapdoor generation algorithm for phrase search and thereby preve nt the leakage due to search pattern. In earlier SSE based schemes, an honest-but-curious server could always learn about the position of the sentences and keywords in the encrypted text after the search operation is performed. This is referred to as the leakage from access pattern. This may turn out to be a significant security concern owing to the prior knowledge of positions of certain sentences and keywords in certain documents. In this paper, we provide the access pattern secure encryption scheme such that, an honest-but-curious cloud server could not learn anything about the position of the phrase in the sentence even after the search. We implement a prototype of our scheme and validate it against commercial data and provide security and performance analysis to demonstrate its practicality.

In this paper, we provide an efficient and easy-to-implement symmetric searchable encryption scheme (SSE) for string search, which takes one round of communication, O(n) times of computations over n documents. Unlike previous schemes, we use hash-chaining instead of chain of encryption operations for index generation, which makes it suitable for lightweight applications. Unlike the previous SSE schemes for string search, with our scheme, server learns nothing about the frequency and the relative positions of the words being searched except what it can learn from the history. We are the first to propose probabilistic trapdoors in SSE for string search. We provide concrete proof of non-adaptive security of our scheme against honest-but-curious server based on the definitions of [12]. We also introduce a new notion of search pattern privacy, which gives a measure of security against the leakage from trapdoor. We have shown that our scheme is secure under search pattern indistinguishability definition. We show why SSE scheme for string search cannot attain adaptive indistinguishability criteria as mentioned in [12]. We also propose modifications of our scheme so that the scheme can be used against active adversaries at the cost of more rounds of communications and memory space. We validate our scheme against two different commercial datasets (see [1], [2]).

We propose the first auctioneer-free sealed-bid auction protocol with a linear computation and communication complexity O(c), c being the bit length of the bid price. Our protocol, called Self-Enforcing Auction Lot (SEAL), operates in a decentralized setting, where bidders jointly compute the maximum bid while preserving the privacy of losing bids. In our protocol, we do not require any secret channels between participants. All operations are publicly verifiable; everyone including third-party observers is able to verify the integrity of the auction outcome. Upon learning the highest bid, the winner comes forward with a proof to prove that she is the real winner. Based on the proof, everyone is able to check if there is only one winner or there is a tie. While our main protocol works with the first-price sealed-bid, it can be easily extended to support the second-price sealed-bid (also known as the Vickrey auction), revealing only the winner and the second highest bid, while keeping the highest bid and all other bids secret. To the best of our knowledge, this work establishes to date the best computation and communication complexity for sealed-bid auction schemes without involving any auctioneer.

This paper introduces an efficient algorithm to develop a privacy-preserving voice verification based on iVector and linear discriminant analysis techniques. This research considers a scenario in which users enrol their voice biometric to access different services (i.e., banking). Once enrolment is completed, users can verify themselves using their voice print instead of alphanumeric passwords. Since a voice print is unique for everyone, storing it with a third-party server raises several privacy concerns. To address this challenge, this paper proposes a novel technique based on randomization to carry out voice authentication, which allows the user to enrol and verify their voice in the randomized domain. To achieve this, the iVector-based voice verification technique has been redesigned to work on the randomized domain. The proposed algorithm is validated using a well-known speech dataset. The proposed algorithm neither compromises the authentication accuracy nor adds additional complexity due to the randomization operations.

Social network platforms have become the new norm for ensuring swift dissemination of information to a large audience. This has thus made these platforms a preferred choice of communication for many criminal organizations who tend to use them for their own iniquitous gains. These organizations take cover behind the user data/identity privacy policies, such as the General Data Protection Regulation (GDPR) [2] and Safe Harbor [4], which limit the Law Enforcement Agencies (LEAs) from accessing and analysing social media data without the consent of the users. Their veil is complemented by the fact that these malicious organization can operate from any part of the world and LEAs from different countries dither in sharing intelligence information among themselves. To overcome this issue, in this paper we propose a novel Privacy-preserving Anonymization with Restricted Search (PARS) approach which will provide LEAs with the leverage they need to access and analyse social media data without compromising individual privacy. We propose a new privacy concious node grouping approach that antagonizes relational information of a social network platform and we compliment this approach with the Public-key Encryption with Keyword Search (PEKS) mechanism that will enable LEAs to perform a restrictive search among each others' dataset without violating the privacy or leakage of the entire dataset to a third party. The proposed approach is applied on a Twitter dataset comprising of 277359 users that comment, re-tweet and/or like 11528 tweets and encryption and search times are evaluated. Furthermore, the proposed approach is tested for the effect of anonymization on information entropy of the twitter dataset.

This paper presents a strategy for enabling speech recognition to be performed in the cloud whilst preserving the privacy of users. The approach advocates a demarcation of responsibilities between the client and server-side components for performing the speech recognition task. On the client-side resides the acoustic model, which symbolically encodes the audio and encrypts the data before uploading to the server. The server-side then employs searchable encryption to enable the phonetic search of the speech content. Some preliminary results for speech encoding and searchable encryption are presented.

S-boxes having large number of linearly independent multivariate biaffine or quadratic equations may be susceptible to certain kinds of algebraic attacks. In a 2009 IEEE-IT paper, Nawaz et al. provided a polynomial time algorithm to compute the number of such equations for finding S-boxes based on power mapping. Finding actual equations in polynomial time was still open. In this paper, techniques for finding a maximal set of linearly independent biaffine and quadratic equations are developed for S-boxes based on power mappings. Two algorithms to calculate the biaffine and quadratic equations for any (n, n) S-box based on power mapping are presented. The time complexity of both the algorithms is O(n6).