Related papers: Secure Single-Server Nearly-Identical Image Dedupl…
We consider the problem of secure distributed matrix computation (SDMC), where a \textit{user} queries a function of data matrices generated at distributed \textit{source} nodes. We assume the availability of $N$ honest but curious…
Location-Based Services (LBSs) provide valuable services, with convenient features for mobile users. However, the location and other information disclosed through each query to the LBS erodes user privacy. This is a concern especially…
The Jaccard index is an important similarity measure for item sets and Boolean data. On large datasets, an exact similarity computation is often infeasible for all item pairs both due to time and space constraints, giving rise to faster…
A privacy-preserving Support Vector Machine (SVM) computing scheme is proposed in this paper. Cloud computing has been spreading in many fields. However, the cloud computing has some serious issues for end users, such as unauthorized use…
High availability is no longer just a business continuity concern. Users are increasingly dependant on devices that consume and produce data in ever increasing volumes. A popular solution is to have a central repository which each device…
Secret sharing is a new alternative for outsourcing data in a secure way.It avoids the need for time consuming encryption decryption process and also the complexity involved in key management.The data must also be protected from untrusted…
Many applications benefit from computations over the data of multiple users while preserving confidentiality. We present a solution where multiple mutually distrusting users' data can be aggregated with an acceptable overhead, while…
In the case that images are shared via social networking services (SNS) and cloud photo sharing services (CPSS), it is known that the JPEG images uploaded to the services are often re-compressed and resized by the providers. Because of such…
Everyday a huge amount of data is generated in Cloud Computing. The maintenance of this electronic data needs some extremely efficient services. There is a need to properly collect this data, check for its authenticity and develop proper…
Deduplication is a vital preprocessing step that enhances machine learning model performance and saves training time and energy. However, enhancing federated learning through deduplication poses challenges, especially regarding scalability…
In recent times, I've encountered a principle known as cloud computing, a model that simplifies user access to data and computing power on a demand basis. The main objective of cloud computing is to accommodate users' growing needs by…
The last decade has witnessed a tremendous growth of service computing, while efficient service recommendation methods are desired to recommend high-quality services to users. It is well known that collaborative filtering is one of the most…
To securely leverage the advantages of Cloud Computing, recently a lot of research has happened in the area of "Secure Query Processing over Encrypted Data". As a concrete use case, many encryption schemes have been proposed for securely…
Federated Learning (FL) enables multiple users to collaboratively train a machine learning model without sharing raw data, making it suitable for privacy-sensitive applications. However, local model or weight updates can still leak…
Inexpensive cloud services, such as serverless computing, are often vulnerable to straggling nodes that increase end-to-end latency for distributed computation. We propose and implement simple yet principled approaches for straggler…
Cloud-device collaboration leverages on-cloud Large Language Models (LLMs) for handling public user queries and on-device Small Language Models (SLMs) for processing private user data, collectively forming a powerful and privacy-preserving…
Driven by the growth of Web-scale decentralized services, Federated Clustering (FC) aims to extract knowledge from heterogeneous clients in an unsupervised manner while preserving the clients' privacy, which has emerged as a significant…
This paper presents a multi-platform, open-source application that aims to protect data stored and shared in existing cloud storage services. The access to the cryptographic material used to protect data is implemented using the…
Decentralized Storage Network (DSN) is an emerging technology that challenges traditional cloud-based storage systems by consolidating storage capacities from independent providers and coordinating to provide decentralized storage and…
Contemporary large language model (LLM) training pipelines require the assembly of internet-scale databases full of text data from a variety of sources (e.g., web, academic, and publishers). Preprocessing these datasets via deduplication --…