4,664 research outputs found
Privacy-preserving recommendation system using federated learning
Federated Learning is a form of distributed learning which leverages edge devices for training. It aims to preserve privacy by communicating users’ learning parameters and gradient updates to the global server during the training while keeping the actual data on the users’ devices. The training on global server is performed on these parameters instead of user data directly while fine tuning of the model can be done on client’s devices locally. However, federated learning is not without its shortcomings and in this thesis, we present an overview of the learning paradigm and propose a new federated recommender system framework that utilizes homomorphic encryption. This results in a slight decrease in accuracy metrics but leads to greatly increased user-privacy. We also show that performing computations on encrypted gradients barely affects the recommendation performance while ensuring a more secure means of communicating user gradients to and from the global server
On the anonymity risk of time-varying user profiles.
Websites and applications use personalisation services to profile their users, collect their patterns and activities and eventually use this data to provide tailored suggestions. User preferences and social interactions are therefore aggregated and analysed. Every time a user publishes a new post or creates a link with another entity, either another user, or some online resource, new information is added to the user profile. Exposing private data does not only reveal information about single users’ preferences, increasing their privacy risk, but can expose more about their network that single actors intended. This mechanism is self-evident in social networks where users receive suggestions based on their friends’ activities. We propose an information-theoretic approach to measure the differential update of the anonymity risk of time-varying user profiles. This expresses how privacy is affected when new content is posted and how much third-party services get to know about the users when a new activity is shared. We use actual Facebook data to show how our model can be applied to a real-world scenario.Peer ReviewedPostprint (published version
On-Device Recommender Systems: A Comprehensive Survey
Recommender systems have been widely deployed in various real-world
applications to help users identify content of interest from massive amounts of
information. Traditional recommender systems work by collecting user-item
interaction data in a cloud-based data center and training a centralized model
to perform the recommendation service. However, such cloud-based recommender
systems (CloudRSs) inevitably suffer from excessive resource consumption,
response latency, as well as privacy and security risks concerning both data
and models. Recently, driven by the advances in storage, communication, and
computation capabilities of edge devices, there has been a shift of focus from
CloudRSs to on-device recommender systems (DeviceRSs), which leverage the
capabilities of edge devices to minimize centralized data storage requirements,
reduce the response latency caused by communication overheads, and enhance user
privacy and security by localizing data processing and model training. Despite
the rapid rise of DeviceRSs, there is a clear absence of timely literature
reviews that systematically introduce, categorize and contrast these methods.
To bridge this gap, we aim to provide a comprehensive survey of DeviceRSs,
covering three main aspects: (1) the deployment and inference of DeviceRSs (2)
the training and update of DeviceRSs (3) the security and privacy of DeviceRSs.
Furthermore, we provide a fine-grained and systematic taxonomy of the methods
involved in each aspect, followed by a discussion regarding challenges and
future research directions. This is the first comprehensive survey on DeviceRSs
that covers a spectrum of tasks to fit various needs. We believe this survey
will help readers effectively grasp the current research status in this field,
equip them with relevant technical foundations, and stimulate new research
ideas for developing DeviceRSs
GraphSE: An Encrypted Graph Database for Privacy-Preserving Social Search
In this paper, we propose GraphSE, an encrypted graph database for online
social network services to address massive data breaches. GraphSE preserves
the functionality of social search, a key enabler for quality social network
services, where social search queries are conducted on a large-scale social
graph and meanwhile perform set and computational operations on user-generated
contents. To enable efficient privacy-preserving social search, GraphSE
provides an encrypted structural data model to facilitate parallel and
encrypted graph data access. It is also designed to decompose complex social
search queries into atomic operations and realise them via interchangeable
protocols in a fast and scalable manner. We build GraphSE with various
queries supported in the Facebook graph search engine and implement a
full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that
GraphSE is practical for querying a social graph with a million of users.Comment: This is the full version of our AsiaCCS paper "GraphSE: An
Encrypted Graph Database for Privacy-Preserving Social Search". It includes
the security proof of the proposed scheme. If you want to cite our work,
please cite the conference version of i
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