86 research outputs found
Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems
Online personalized recommendation services are generally hosted in the cloud
where users query the cloud-based model to receive recommended input such as
merchandise of interest or news feed. State-of-the-art recommendation models
rely on sparse and dense features to represent users' profile information and
the items they interact with. Although sparse features account for 99% of the
total model size, there was not enough attention paid to the potential
information leakage through sparse features. These sparse features are employed
to track users' behavior, e.g., their click history, object interactions, etc.,
potentially carrying each user's private information. Sparse features are
represented as learned embedding vectors that are stored in large tables, and
personalized recommendation is performed by using a specific user's sparse
feature to index through the tables. Even with recently-proposed methods that
hides the computation happening in the cloud, an attacker in the cloud may be
able to still track the access patterns to the embedding tables. This paper
explores the private information that may be learned by tracking a
recommendation model's sparse feature access patterns. We first characterize
the types of attacks that can be carried out on sparse features in
recommendation models in an untrusted cloud, followed by a demonstration of how
each of these attacks leads to extracting users' private information or
tracking users by their behavior over time
LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization
Federated recommender system (FRS), which enables many local devices to train
a shared model jointly without transmitting local raw data, has become a
prevalent recommendation paradigm with privacy-preserving advantages. However,
previous work on FRS performs similarity search via inner product in continuous
embedding space, which causes an efficiency bottleneck when the scale of items
is extremely large. We argue that such a scheme in federated settings ignores
the limited capacities in resource-constrained user devices (i.e., storage
space, computational overhead, and communication bandwidth), and makes it
harder to be deployed in large-scale recommender systems. Besides, it has been
shown that transmitting local gradients in real-valued form between server and
clients may leak users' private information. To this end, we propose a
lightweight federated recommendation framework with privacy-preserving matrix
factorization, LightFR, that is able to generate high-quality binary codes by
exploiting learning to hash technique under federated settings, and thus enjoys
both fast online inference and economic memory consumption. Moreover, we devise
an efficient federated discrete optimization algorithm to collaboratively train
model parameters between the server and clients, which can effectively prevent
real-valued gradient attacks from malicious parties. Through extensive
experiments on four real-world datasets, we show that our LightFR model
outperforms several state-of-the-art FRS methods in terms of recommendation
accuracy, inference efficiency and data privacy.Comment: Accepted by ACM Transactions on Information Systems (TOIS
Building K-Anonymous User Cohorts with\\ Consecutive Consistent Weighted Sampling (CCWS)
To retrieve personalized campaigns and creatives while protecting user
privacy, digital advertising is shifting from member-based identity to
cohort-based identity. Under such identity regime, an accurate and efficient
cohort building algorithm is desired to group users with similar
characteristics. In this paper, we propose a scalable -anonymous cohort
building algorithm called {\em consecutive consistent weighted sampling}
(CCWS). The proposed method combines the spirit of the (-powered) consistent
weighted sampling and hierarchical clustering, so that the -anonymity is
ensured by enforcing a lower bound on the size of cohorts. Evaluations on a
LinkedIn dataset consisting of M users and ads campaigns demonstrate that
CCWS achieves substantial improvements over several hashing-based methods
including sign random projections (SignRP), minwise hashing (MinHash), as well
as the vanilla CWS
Blockchain-based recommender systems: Applications, challenges and future opportunities
Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models' accuracy and ignore issues related to security and the users' privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users' private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research. 2021 Elsevier Inc.This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu
Modeling Time-Series and Spatial Data for Recommendations and Other Applications
With the research directions described in this thesis, we seek to address the
critical challenges in designing recommender systems that can understand the
dynamics of continuous-time event sequences. We follow a ground-up approach,
i.e., first, we address the problems that may arise due to the poor quality of
CTES data being fed into a recommender system. Later, we handle the task of
designing accurate recommender systems. To improve the quality of the CTES
data, we address a fundamental problem of overcoming missing events in temporal
sequences. Moreover, to provide accurate sequence modeling frameworks, we
design solutions for points-of-interest recommendation, i.e., models that can
handle spatial mobility data of users to various POI check-ins and recommend
candidate locations for the next check-in. Lastly, we highlight that the
capabilities of the proposed models can have applications beyond recommender
systems, and we extend their abilities to design solutions for large-scale CTES
retrieval and human activity prediction. A significant part of this thesis uses
the idea of modeling the underlying distribution of CTES via neural marked
temporal point processes (MTPP). Traditional MTPP models are stochastic
processes that utilize a fixed formulation to capture the generative mechanism
of a sequence of discrete events localized in continuous time. In contrast,
neural MTPP combine the underlying ideas from the point process literature with
modern deep learning architectures. The ability of deep-learning models as
accurate function approximators has led to a significant gain in the predictive
prowess of neural MTPP models. In this thesis, we utilize and present several
neural network-based enhancements for the current MTPP frameworks for the
aforementioned real-world applications.Comment: Ph.D. Thesis (2022
Recent Developments in Recommender Systems: A Survey
In this technical survey, we comprehensively summarize the latest
advancements in the field of recommender systems. The objective of this study
is to provide an overview of the current state-of-the-art in the field and
highlight the latest trends in the development of recommender systems. The
study starts with a comprehensive summary of the main taxonomy of recommender
systems, including personalized and group recommender systems, and then delves
into the category of knowledge-based recommender systems. In addition, the
survey analyzes the robustness, data bias, and fairness issues in recommender
systems, summarizing the evaluation metrics used to assess the performance of
these systems. Finally, the study provides insights into the latest trends in
the development of recommender systems and highlights the new directions for
future research in the field
Edge AI for Internet of Energy: Challenges and Perspectives
The digital landscape of the Internet of Energy (IoE) is on the brink of a
revolutionary transformation with the integration of edge Artificial
Intelligence (AI). This comprehensive review elucidates the promise and
potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a
meticulously curated research methodology, the article delves into the myriad
of edge AI techniques specifically tailored for IoE. The myriad benefits,
spanning from reduced latency and real-time analytics to the pivotal aspects of
information security, scalability, and cost-efficiency, underscore the
indispensability of edge AI in modern IoE frameworks. As the narrative
progresses, readers are acquainted with pragmatic applications and techniques,
highlighting on-device computation, secure private inference methods, and the
avant-garde paradigms of AI training on the edge. A critical analysis follows,
offering a deep dive into the present challenges including security concerns,
computational hurdles, and standardization issues. However, as the horizon of
technology ever expands, the review culminates in a forward-looking
perspective, envisaging the future symbiosis of 5G networks, federated edge AI,
deep reinforcement learning, and more, painting a vibrant panorama of what the
future beholds. For anyone vested in the domains of IoE and AI, this review
offers both a foundation and a visionary lens, bridging the present realities
with future possibilities
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