Temporal fingerprints: Identity matching across fully encrypted domain
- URL: http://arxiv.org/abs/2407.04350v1
- Date: Fri, 5 Jul 2024 08:41:28 GMT
- Title: Temporal fingerprints: Identity matching across fully encrypted domain
- Authors: Shahar Somin, Keeley Erhardt, Alex 'Sandy' Pentland,
- Abstract summary: Cross domain identity matching is essential for practical applications and theoretical insights into the privacy implications of data disclosure.
We demonstrate that individual temporal data, in the form of inter-event times distribution, constitutes an individual temporal fingerprint, allowing for matching profiles across different domains back to their associated real-world entity.
Our findings indicate that simply knowing when an individual is active, even if information about who they talk to and what they discuss is lacking, poses risks to users' privacy.
- Score: 6.44378713940627
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Technological advancements have significantly transformed communication patterns, introducing a diverse array of online platforms, thereby prompting individuals to use multiple profiles for different domains and objectives. Enhancing the understanding of cross domain identity matching capabilities is essential, not only for practical applications such as commercial strategies and cybersecurity measures, but also for theoretical insights into the privacy implications of data disclosure. In this study, we demonstrate that individual temporal data, in the form of inter-event times distribution, constitutes an individual temporal fingerprint, allowing for matching profiles across different domains back to their associated real-world entity. We evaluate our methodology on encrypted digital trading platforms within the Ethereum Blockchain and present impressing results in matching identities across these privacy-preserving domains, while outperforming previously suggested models. Our findings indicate that simply knowing when an individual is active, even if information about who they talk to and what they discuss is lacking, poses risks to users' privacy, highlighting the inherent challenges in preserving privacy in today's digital landscape.
Related papers
- Masked Differential Privacy [64.32494202656801]
We propose an effective approach called masked differential privacy (DP), which allows for controlling sensitive regions where differential privacy is applied.
Our method operates selectively on data and allows for defining non-sensitive-temporal regions without DP application or combining differential privacy with other privacy techniques within data samples.
arXiv Detail & Related papers (2024-10-22T15:22:53Z) - Centering Policy and Practice: Research Gaps around Usable Differential Privacy [12.340264479496375]
We argue that while differential privacy is a clean formulation in theory, it poses significant challenges in practice.
To bridge the gaps between differential privacy's promises and its real-world usability, researchers and practitioners must work together.
arXiv Detail & Related papers (2024-06-17T21:32:30Z) - PrivacyMind: Large Language Models Can Be Contextual Privacy Protection Learners [81.571305826793]
We introduce Contextual Privacy Protection Language Models (PrivacyMind)
Our work offers a theoretical analysis for model design and benchmarks various techniques.
In particular, instruction tuning with both positive and negative examples stands out as a promising method.
arXiv Detail & Related papers (2023-10-03T22:37:01Z) - A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing.
Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data.
Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
arXiv Detail & Related papers (2023-09-27T14:38:16Z) - Diff-Privacy: Diffusion-based Face Privacy Protection [58.1021066224765]
In this paper, we propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy.
Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image.
Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding.
arXiv Detail & Related papers (2023-09-11T09:26:07Z) - Disguise without Disruption: Utility-Preserving Face De-Identification [40.484745636190034]
We introduce Disguise, a novel algorithm that seamlessly de-identifies facial images while ensuring the usability of the modified data.
Our method involves extracting and substituting depicted identities with synthetic ones, generated using variational mechanisms to maximize obfuscation and non-invertibility.
We extensively evaluate our method using multiple datasets, demonstrating a higher de-identification rate and superior consistency compared to prior approaches in various downstream tasks.
arXiv Detail & Related papers (2023-03-23T13:50:46Z) - Reinforcement Learning on Encrypted Data [58.39270571778521]
We present a preliminary, experimental study of how a DQN agent trained on encrypted states performs in environments with discrete and continuous state spaces.
Our results highlight that the agent is still capable of learning in small state spaces even in presence of non-deterministic encryption, but performance collapses in more complex environments.
arXiv Detail & Related papers (2021-09-16T21:59:37Z) - IdentityDP: Differential Private Identification Protection for Face
Images [17.33916392050051]
Face de-identification, also known as face anonymization, refers to generating another image with similar appearance and the same background, while the real identity is hidden.
We propose IdentityDP, a face anonymization framework that combines a data-driven deep neural network with a differential privacy mechanism.
Our model can effectively obfuscate the identity-related information of faces, preserve significant visual similarity, and generate high-quality images.
arXiv Detail & Related papers (2021-03-02T14:26:00Z) - Learning With Differential Privacy [3.618133010429131]
Differential privacy comes to the rescue with a proper promise of protection against leakage.
It uses a randomized response technique at the time of collection of the data which promises strong privacy with better utility.
arXiv Detail & Related papers (2020-06-10T02:04:13Z) - InfoScrub: Towards Attribute Privacy by Targeted Obfuscation [77.49428268918703]
We study techniques that allow individuals to limit the private information leaked in visual data.
We tackle this problem in a novel image obfuscation framework.
We find our approach generates obfuscated images faithful to the original input images, and additionally increase uncertainty by 6.2$times$ (or up to 0.85 bits) over the non-obfuscated counterparts.
arXiv Detail & Related papers (2020-05-20T19:48:04Z)
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.