Advancing Recommender Systems by mitigating Shilling attacks

• URL: http://arxiv.org/abs/2404.16177v1
• Date: Wed, 24 Apr 2024 20:05:39 GMT
• Title: Advancing Recommender Systems by mitigating Shilling attacks
• Authors: Aditya Chichani, Juzer Golwala, Tejas Gundecha, Kiran Gawande,
• Abstract summary: Collaborative filtering is a widely used method for computing recommendations due to its good performance. This paper proposes an algorithm to detect such shilling profiles in the system accurately and also study the effects of such profiles on the recommendations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Considering the premise that the number of products offered grow in an exponential fashion and the amount of data that a user can assimilate before making a decision is relatively small, recommender systems help in categorizing content according to user preferences. Collaborative filtering is a widely used method for computing recommendations due to its good performance. But, this method makes the system vulnerable to attacks which try to bias the recommendations. These attacks, known as 'shilling attacks' are performed to push an item or nuke an item in the system. This paper proposes an algorithm to detect such shilling profiles in the system accurately and also study the effects of such profiles on the recommendations.

Related papers

• Shadow-Free Membership Inference Attacks: Recommender Systems Are More Vulnerable Than You Thought [43.490918008927]
  We propose shadow-free MIAs that directly leverage a user's recommendations for membership inference. Our attack achieves far better attack accuracy with low false positive rates than baselines.
  arXiv  Detail & Related papers  (2024-05-11T13:52:22Z)
• PORE: Provably Robust Recommender Systems against Data Poisoning Attacks [58.26750515059222]
  We propose PORE, the first framework to build provably robust recommender systems. PORE can transform any existing recommender system to be provably robust against untargeted data poisoning attacks. We prove that PORE still recommends at least $r$ of the $N$ items to the user under any data poisoning attack, where $r$ is a function of the number of fake users in the attack.
  arXiv  Detail & Related papers  (2023-03-26T01:38:11Z)
• Breaking Feedback Loops in Recommender Systems with Causal Inference [99.22185950608838]
  Recent work has shown that feedback loops may compromise recommendation quality and homogenize user behavior. We propose the Causal Adjustment for Feedback Loops (CAFL), an algorithm that provably breaks feedback loops using causal inference. We show that CAFL improves recommendation quality when compared to prior correction methods.
  arXiv  Detail & Related papers  (2022-07-04T17:58:39Z)
• Recommendation Systems with Distribution-Free Reliability Guarantees [83.80644194980042]
  We show how to return a set of items rigorously guaranteed to contain mostly good items. Our procedure endows any ranking model with rigorous finite-sample control of the false discovery rate. We evaluate our methods on the Yahoo! Learning to Rank and MSMarco datasets.
  arXiv  Detail & Related papers  (2022-07-04T17:49:25Z)
• Debiasing Learning for Membership Inference Attacks Against Recommender Systems [79.48353547307887]
  Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems through the lens of membership inference. We propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components.
  arXiv  Detail & Related papers  (2022-06-24T17:57:34Z)
• Introducing a Framework and a Decision Protocol to Calibrate Recommender Systems [0.0]
  This paper proposes an approach to create recommendation lists with a calibrated balance of genres. The main claim is that calibration can contribute positively to generate fairer recommendations. We propose a conceptual framework and a decision protocol to generate more than one thousand combinations of calibrated systems.
  arXiv  Detail & Related papers  (2022-04-07T19:30:55Z)
• PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion [58.870444954499014]
  A common practice is to subsume recommender systems under the decentralized federated learning paradigm. We present a systematic approach to backdooring federated recommender systems for targeted item promotion.
  arXiv  Detail & Related papers  (2021-10-21T06:48:35Z)
• Membership Inference Attacks Against Recommender Systems [33.66394989281801]
  We make the first attempt on quantifying the privacy leakage of recommender systems through the lens of membership inference. Our attack is on the user-level but not on the data sample-level. A shadow recommender is established to derive the labeled training data for training the attack model.
  arXiv  Detail & Related papers  (2021-09-16T15:19:19Z)
• On Detecting Data Pollution Attacks On Recommender Systems Using Sequential GANs [9.497749999148107]
  A malicious actor may be motivated to sway the output of recommender systems by injecting malicious datapoints. We propose a semi-supervised attack detection algorithm to identify the malicious datapoints.
  arXiv  Detail & Related papers  (2020-12-04T10:31:28Z)
• Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [48.2733163413522]
  It becomes critical to embrace a trustworthy recommender system. This survey provides a systemic summary of three categories of trust-aware recommender systems.
  arXiv  Detail & Related papers  (2020-04-08T02:11:55Z)
• Influence Function based Data Poisoning Attacks to Top-N Recommender Systems [43.14766256772]
  An attacker can trick a recommender system to recommend a target item to as many normal users as possible. We develop a data poisoning attack to solve this problem. Our results show that our attacks are effective and outperform existing methods.
  arXiv  Detail & Related papers  (2020-02-19T06:41:51Z)

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.