744 research outputs found
Data-Driven and Deep Learning Methodology for Deceptive Advertising and Phone Scams Detection
The advance of smartphones and cellular networks boosts the need of mobile
advertising and targeted marketing. However, it also triggers the unseen
security threats. We found that the phone scams with fake calling numbers of
very short lifetime are increasingly popular and have been used to trick the
users. The harm is worldwide. On the other hand, deceptive advertising
(deceptive ads), the fake ads that tricks users to install unnecessary apps via
either alluring or daunting texts and pictures, is an emerging threat that
seriously harms the reputation of the advertiser. To counter against these two
new threats, the conventional blacklist (or whitelist) approach and the machine
learning approach with predefined features have been proven useless.
Nevertheless, due to the success of deep learning in developing the highly
intelligent program, our system can efficiently and effectively detect phone
scams and deceptive ads by taking advantage of our unified framework on deep
neural network (DNN) and convolutional neural network (CNN). The proposed
system has been deployed for operational use and the experimental results
proved the effectiveness of our proposed system. Furthermore, we keep our
research results and release experiment material on
http://DeceptiveAds.TWMAN.ORG and http://PhoneScams.TWMAN.ORG if there is any
update.Comment: 6 pages, TAAI 2017 versio
Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers
For machine learning with tabular data, Table Transformer (TabTransformer) is
a state-of-the-art neural network model, while Differential Privacy (DP) is an
essential component to ensure data privacy. In this paper, we explore the
benefits of combining these two aspects together in the scenario of transfer
learning -- differentially private pre-training and fine-tuning of
TabTransformers with a variety of parameter-efficient fine-tuning (PEFT)
methods, including Adapter, LoRA, and Prompt Tuning. Our extensive experiments
on the ACSIncome dataset show that these PEFT methods outperform traditional
approaches in terms of the accuracy of the downstream task and the number of
trainable parameters, thus achieving an improved trade-off among parameter
efficiency, privacy, and accuracy. Our code is available at
github.com/IBM/DP-TabTransformer.Comment: submitted to ICASSP 202
XDedup: Efficient Provably-Secure Cross-User Chunk-Level Client-Side Deduplicated Cloud Storage of Encrypted Data
Data deduplication, aiming to eliminate duplicate data, has been widely used in cloud storage to reduce the amount of storage space and save bandwidth. Unfortunately, as an increasing number of sensitive data are stored remotely, the encryption, the simplest way for data privacy, is not compatible with data deduplication. Though many research efforts have been devoted to securing deduplication, they all are subject to performance, security, and applicability limitations. Here, we propose two encrypted deduplication schemes, SDedup and XDedup, both based on Merkle puzzle. To the best of our knowledge, XDedup is the first brute-force resilient encrypted deduplication with only symmetrically cryptographic two-party interactions. The analysis and numerical simulations are conducted to demonstrate the performance and practicality of SDedup and XDedup
- …