Faculty Publications

Now showing 1 - 3 of 3

Keyword-based private searching on cloud data along with keyword association and dissociation using cuckoo filter
(Springer Verlag service@springer.de, 2019) Vora, A.V.; Hegde, S.
Outsourcing of data is a very common scenario in the present-day world and quite often we need to outsource confidential data whose privacy is of utmost concern. Performing encryption before outsourcing the data is a simple solution to preserve privacy. Preferably a public-key encryption technique is used to encrypt the data. A demerit of encrypting data is that while requesting the data from the cloud we need to have some technique which supports search functionality on encrypted data. Without the searchable encryption technique, the cloud is forced to send the whole database, which is highly inefficient and impractical. To address this problem, we consider the email scenario, in which the sender of the email will encrypt email contents using receiver’s public key; hence, only the receiver can decrypt email contents. We propose a scheme that will have encrypted emails stored on the cloud and have capabilities that support searching through the encrypted database. This enables the cloud to reply to a request with a more precise response without compromising any privacy in terms of email contents and also in terms of access patterns. We provide a solution for the email scenario in which we can tag or associate emails with some keywords, and during retrieval, the email owner can request all the emails associated with a particular keyword. Although attempts are seen in the literature to solve this issue they do not have the flexibility of dissociating keywords from an email. Keyword dissociation is essential to modify the association between keywords and emails to enable better filtering of emails. Our technique also supports the functionality of keyword dissociation. The solution allows single-database private information retrieval writing in an oblivious way with sublinear communication cost. We have theoretically proved the correctness and security of our technique. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
On “Practical and secure outsourcing algorithms for solving quadratic congruences in IoTs” from IEEE IoT journal
(Elsevier B.V., 2021) Ramalingam, J.
Secure outsourced computation enables IoT devices to offload resource-intensive computations to a more resourceful server while keeping the inputs secret to the server. Recently, Zhang et al. put forth two outsourcing algorithms for solving quadratic congruences (Zhang et al., 2020). We observe that both the algorithms do not achieve the claimed security guarantees: a polynomial-time attack reveals the secret inputs to a passive adversary. As a consequence of the insecure outsourcing, the factorization of the RSA modulus is also revealed and hence leads to the total compromise of the security of the underlying scheme which makes use of the Zhang et al. outsourcing algorithms for solving quadratic congruences. Interestingly, we propose corrective measures for the Zhang et al. algorithm and prove that the resulting algorithm enables secure and verifiable delegation of solving quadratic congruences in IoTs. © 2021 Elsevier B.V.
A Note on “Secure and Efficient Outsourcing of PCA-Based Face Recognition”
(Institute of Electrical and Electronics Engineers Inc., 2025) Rath, S.; Ramalingam, J.; Seal, S.
Zhang et al. (2020) exhibit a fundamental mathematical flaw that renders their algorithm infeasible. Additionally, existing outsourcing protocols for PCA-based face recognition suffer from inadequate verification methods, undermining the reliability of these algorithms. © 2005-2012 IEEE.

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