PRSI: Privacy-Preserving Recommendation Model Based on Vector Splitting and Interactive Protocols
- URL: http://arxiv.org/abs/2411.18653v1
- Date: Wed, 27 Nov 2024 05:14:15 GMT
- Title: PRSI: Privacy-Preserving Recommendation Model Based on Vector Splitting and Interactive Protocols
- Authors: Xiaokai Cao, Wenjin Mo, Zhenyu He, Changdong Wang,
- Abstract summary: This paper proposes a new privacy-preserving recommendation system (PRSI)<n>The two main phases are: (1) the collection of interaction information and (2) the sending of recommendation results.
- Score: 13.36060473598037
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the development of the internet, recommending interesting products to users has become a highly valuable research topic for businesses. Recommendation systems play a crucial role in addressing this issue. To prevent the leakage of each user's (client's) private data, Federated Recommendation Systems (FedRec) have been proposed and widely used. However, extensive research has shown that FedRec suffers from security issues such as data privacy leakage, and it is challenging to train effective models with FedRec when each client only holds interaction information for a single user. To address these two problems, this paper proposes a new privacy-preserving recommendation system (PRSI), which includes a preprocessing module and two main phases. The preprocessing module employs split vectors and fake interaction items to protect clients' interaction information and recommendation results. The two main phases are: (1) the collection of interaction information and (2) the sending of recommendation results. In the interaction information collection phase, each client uses the preprocessing module and random communication methods (according to the designed interactive protocol) to protect their ID information and IP addresses. In the recommendation results sending phase, the central server uses the preprocessing module and triplets to distribute recommendation results to each client under secure conditions, following the designed interactive protocol. Finally, we conducted multiple sets of experiments to verify the security, accuracy, and communication cost of the proposed method.
Related papers
- RecPS: Privacy Risk Scoring for Recommender Systems [4.772368796656325]
We propose a membership-inference attack (MIA)-based privacy scoring method, RecPS, to measure privacy risks at both the interaction and user levels.<n>A critical component is the interaction-level MIA method RecLiRA, which gives high-quality membership estimation.
arXiv Detail & Related papers (2025-07-24T12:46:30Z) - Multi-agents based User Values Mining for Recommendation [52.26100802380767]
We propose a zero-shot multi-LLM collaborative framework for effective and accurate user value extraction.<n>We apply text summarization techniques to condense item content while preserving essential meaning.<n>To mitigate hallucinations, we introduce two specialized agent roles: evaluators and supervisors.
arXiv Detail & Related papers (2025-05-02T04:01:31Z) - Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User [117.82681846559909]
Conversational recommendation systems (CRSs) use multi-turn interaction to capture user preferences and provide personalized recommendations.
We propose a generative reward model based simulated user, named GRSU, for automatic interaction with CRSs.
arXiv Detail & Related papers (2025-04-29T06:37:30Z) - FedCIA: Federated Collaborative Information Aggregation for Privacy-Preserving Recommendation [28.8047308546416]
We introduce the federated collaborative information aggregation (FedCIA) method for privacy-preserving recommendation.
FedCIA allows clients to align their local models without constraining embeddings to a unified vector space.
It mitigates information loss caused by direct summation, preserves the personalized embedding distributions of individual clients, and supports the aggregation of parameter-free models.
arXiv Detail & Related papers (2025-04-19T06:59:34Z) - Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning [54.20871516148981]
We introduce the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM)<n>CEPAM achieves communication efficiency and privacy protection simultaneously.<n>We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM.
arXiv Detail & Related papers (2025-01-21T11:16:05Z) - InterFormer: Towards Effective Heterogeneous Interaction Learning for Click-Through Rate Prediction [72.50606292994341]
We propose a novel module named InterFormer to learn heterogeneous information interaction in an interleaving style.
Our proposed InterFormer achieves state-of-the-art performance on three public datasets and a large-scale industrial dataset.
arXiv Detail & Related papers (2024-11-15T00:20:36Z) - PDC-FRS: Privacy-preserving Data Contribution for Federated Recommender System [15.589541738576528]
Federated recommender systems (FedRecs) have emerged as a popular research direction for protecting users' privacy in on-device recommendations.
In FedRecs, users keep their data locally and only contribute their local collaborative information by uploading model parameters to a central server.
We propose a novel federated recommendation framework, PDC-FRS. Specifically, we design a privacy-preserving data contribution mechanism that allows users to share their data with a differential privacy guarantee.
arXiv Detail & Related papers (2024-09-12T06:13:07Z) - ACCESS-FL: Agile Communication and Computation for Efficient Secure Aggregation in Stable Federated Learning Networks [26.002975401820887]
Federated Learning (FL) is a distributed learning framework designed for privacy-aware applications.
Traditional FL approaches risk exposing sensitive client data when plain model updates are transmitted to the server.
Google's Secure Aggregation (SecAgg) protocol addresses this threat by employing a double-masking technique.
We propose ACCESS-FL, a communication-and-computation-efficient secure aggregation method.
arXiv Detail & Related papers (2024-09-03T09:03:38Z) - Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation [18.0700584280752]
Cross-domain recommendation (CDR) aims to address the data-sparsity problem by transferring knowledge across domains.
Recent privacy-preserving CDR models have been proposed to solve this problem.
We propose a novel Federated User Preference Modeling (FUPM) framework.
arXiv Detail & Related papers (2024-08-26T23:29:03Z) - PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System [42.79538136366075]
This paper proposes a parameter transmission-free federated sequential recommendation framework (PTF-FSR)
PTF-FSR ensures both model and data privacy protection to meet the privacy needs of service providers and system users alike.
arXiv Detail & Related papers (2024-06-08T07:45:46Z) - Prompt-based Personalized Federated Learning for Medical Visual Question
Answering [56.002377299811656]
We present a novel prompt-based personalized federated learning (pFL) method to address data heterogeneity and privacy concerns.
We regard medical datasets from different organs as clients and use pFL to train personalized transformer-based VQA models for each client.
arXiv Detail & Related papers (2024-02-15T03:09:54Z) - On Generative Agents in Recommendation [58.42840923200071]
Agent4Rec is a user simulator in recommendation based on Large Language Models.
Each agent interacts with personalized recommender models in a page-by-page manner.
arXiv Detail & Related papers (2023-10-16T06:41:16Z) - AgentCF: Collaborative Learning with Autonomous Language Agents for
Recommender Systems [112.76941157194544]
We propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering.
We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimize both kinds of agents together.
Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions.
arXiv Detail & Related papers (2023-10-13T16:37:14Z) - BARCOR: Towards A Unified Framework for Conversational Recommendation
Systems [40.464281243375815]
We propose a unified framework based on BART for conversational recommendation.
We also design and collect a lightweight knowledge graph for CRS in the movie domain.
arXiv Detail & Related papers (2022-03-27T09:42:16Z) - Controllable Multi-Interest Framework for Recommendation [64.30030600415654]
We formalize the recommender system as a sequential recommendation problem.
We propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec.
Our framework has been successfully deployed on the offline Alibaba distributed cloud platform.
arXiv Detail & Related papers (2020-05-19T10:18:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.