AI Delegates with a Dual Focus: Ensuring Privacy and Strategic Self-Disclosure

• URL: http://arxiv.org/abs/2409.17642v2
• Date: Mon, 7 Oct 2024 06:29:54 GMT
• Title: AI Delegates with a Dual Focus: Ensuring Privacy and Strategic Self-Disclosure
• Authors: Xi Chen, Zhiyang Zhang, Fangkai Yang, Xiaoting Qin, Chao Du, Xi Cheng, Hangxin Liu, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang,
• Abstract summary: We conduct a pilot study to investigate user preferences for AI delegates across various social relations and task scenarios. We then propose a novel AI delegate system that enables privacy-conscious self-disclosure. Our user study demonstrates that the proposed AI delegate strategically protects privacy, pioneering its use in diverse and dynamic social interactions.
• Score: 42.96087647326612
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM)-based AI delegates are increasingly utilized to act on behalf of users, assisting them with a wide range of tasks through conversational interfaces. Despite their advantages, concerns arise regarding the potential risk of privacy leaks, particularly in scenarios involving social interactions. While existing research has focused on protecting privacy by limiting the access of AI delegates to sensitive user information, many social scenarios require disclosing private details to achieve desired outcomes, necessitating a balance between privacy protection and disclosure. To address this challenge, we conduct a pilot study to investigate user preferences for AI delegates across various social relations and task scenarios, and then propose a novel AI delegate system that enables privacy-conscious self-disclosure. Our user study demonstrates that the proposed AI delegate strategically protects privacy, pioneering its use in diverse and dynamic social interactions.

Related papers

• Collaborative Inference over Wireless Channels with Feature Differential Privacy [57.68286389879283]
  Collaborative inference among multiple wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications. transmitting extracted features poses a significant privacy risk, as sensitive personal data can be exposed during the process. We propose a novel privacy-preserving collaborative inference mechanism, wherein each edge device in the network secures the privacy of extracted features before transmitting them to a central server for inference.
  arXiv  Detail & Related papers  (2024-10-25T18:11:02Z)
• Assessing Privacy Policies with AI: Ethical, Legal, and Technical Challenges [6.916147439085307]
  Large Language Models (LLMs) can be used to assess privacy policies for users automatically. We explore the challenges of this approach in three pillars, namely technical feasibility, ethical implications, and legal compatibility. Our findings aim to identify potential for future research, and to foster a discussion on the use of LLM technologies.
  arXiv  Detail & Related papers  (2024-10-10T21:36:35Z)
• Privacy Risks of General-Purpose AI Systems: A Foundation for Investigating Practitioner Perspectives [47.17703009473386]
  Powerful AI models have led to impressive leaps in performance across a wide range of tasks. Privacy concerns have led to a wealth of literature covering various privacy risks and vulnerabilities of AI models. We conduct a systematic review of these survey papers to provide a concise and usable overview of privacy risks in GPAIS.
  arXiv  Detail & Related papers  (2024-07-02T07:49:48Z)
• Experts-in-the-Loop: Establishing an Effective Workflow in Crafting Privacy Q&A [0.0]
  We propose a dynamic workflow for transforming privacy policies into privacy question-and-answer (Q&A) pairs. Thereby, we facilitate interdisciplinary collaboration among legal experts and conversation designers. Our proposed workflow underscores continuous improvement and monitoring throughout the construction of privacy Q&As.
  arXiv  Detail & Related papers  (2023-11-18T20:32:59Z)
• Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees [81.6471440778355]
  We propose a Cross-network Social User Embedding framework, namely DP-CroSUE, to learn the comprehensive representations of users in a privacy-preserving way. In particular, for each heterogeneous social network, we first introduce a hybrid differential privacy notion to capture the variation of privacy expectations for heterogeneous data types. To further enhance user embeddings, a novel cross-network GCN embedding model is designed to transfer knowledge across networks through those aligned users.
  arXiv  Detail & Related papers  (2022-09-04T06:22:37Z)
• Privacy in Open Search: A Review of Challenges and Solutions [0.6445605125467572]
  Information retrieval (IR) is prone to privacy threats, such as attacks and unintended disclosures of documents and search history. This work aims at highlighting and discussing open challenges for privacy in the recent literature of IR, focusing on tasks featuring user-generated text data.
  arXiv  Detail & Related papers  (2021-10-20T18:38:48Z)
• Persuasion Meets AI: Ethical Considerations for the Design of Social Engineering Countermeasures [0.0]
  Privacy in Social Network Sites (SNSs) like Facebook or Instagram is closely related to people's self-disclosure decisions. Online privacy decisions are often based on spurious risk judgements that make people liable to reveal sensitive data to untrusted recipients. This paper elaborates on the ethical challenges that nudging mechanisms can introduce to the development of AI-based countermeasures.
  arXiv  Detail & Related papers  (2020-09-27T14:24:29Z)
• Differentially Private Multi-Agent Planning for Logistic-like Problems [70.3758644421664]
  This paper proposes a novel strong privacy-preserving planning approach for logistic-like problems. Two challenges are addressed: 1) simultaneously achieving strong privacy, completeness and efficiency, and 2) addressing communication constraints. To the best of our knowledge, this paper is the first to apply differential privacy to the field of multi-agent planning.
  arXiv  Detail & Related papers  (2020-08-16T03:43:09Z)
• More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence [62.3133247463974]
  We show that differential privacy can do more than just privacy preservation in AI. It can also be used to improve security, stabilize learning, build fair models, and impose composition in selected areas of AI.
  arXiv  Detail & Related papers  (2020-08-05T03:07:36Z)

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.