arXiv subjects

Scott Jordan

Publications and source records attributed to Scott Jordan.

8 recordsLinked to original sources

arXiv2024

DiffAudit: Auditing Privacy Practices of Online Services for Children and Adolescents

Children's and adolescents' online data privacy are regulated by laws such as the Children's Online Privacy Protection Act (COPPA) and the California Consumer Privacy Act (CCPA). Online services that are directed towards general audiences (i.e., including children, adolescents, and adults) must comply with these laws. In this paper, first, we present DiffAudit, a platform-agnostic privacy auditing methodology for general audience services. DiffAudit performs differential analysis of network traffic data flows to compare data processing practices (i) between child, adolescent, and adult users and (ii) before and after consent is given and user age is disclosed. We also present a data type classification method that utilizes GPT-4 and our data type ontology based on COPPA and CCPA, allowing us to identify considerably more data types than prior work. Second, we apply DiffAudit to a set of popular general audience mobile and web services and observe a rich set of behaviors extracted from over 440K outgoing requests, containing 3,968 unique data types we extracted and classified. We reveal problematic data processing practices prior to consent and age disclosure, lack of differentiation between age-specific data flows, inconsistent privacy policy disclosures, and sharing of linkable data with third parties, including advertising and tracking services.

cs.CR↗

arXiv2023

Peering Costs and Fees

Internet users have suffered collateral damage in tussles over paid peering between large ISPs and large content providers. In order to qualify for settlement-free peering, large Internet Service Providers (ISPs) require that peers meet certain requirements. However, the academic literature has not yet shown the relationship between these settlement-free peering requirements and the value to each interconnecting network. We first consider the effect of paid peering on broadband prices. We adopt a two-sided market model in which an ISP maximizes profit by setting broadband prices and a paid peering price. Our result shows that paid peering fees reduce the premium plan price, and increase the video streaming price and the total price for premium tier customers who subscribe to video streaming services. We next consider the effect of paid peering on consumer surplus. We find that consumer surplus is a uni-modal function of the paid peering fee. The peering price depends critically on the incremental ISP cost per video streaming subscriber; at different costs, it can be negative, zero, or positive. Last, we construct a network cost model. We show that the traffic-sensitive network cost decreases as the number of interconnection points increases, but with decreasing returns. Interconnecting at 6 to 8 interconnection points is rational, and requiring interconnection at more than 8 points is of little value. We show that if the content delivery network (CDN) delivers traffic to the ISP locally, then a requirement to interconnect at a minimum number of interconnection points is rational. We also show that if the CDN delivers traffic using hot potato routing, the ISP is unlikely to perceive sufficient value to offer settlement-free peering.

cs.NI↗

arXiv2023

Towards Equitable Peering: A Proposal for a Fair Peering Fee Between ISPs and Content Providers

Disagreements over peering fees have risen to the level of potential government regulation. ISPs assert that content providers should pay them based on the volume of downstream traffic. Transit providers and content providers assert that consumers have already paid ISPs to transmit the content they request and that peering agreements should be settlement-free. Our goal is to determine the fair payment between an ISP and an interconnecting network. We consider fair cost sharing between two Tier-1 ISPs, and derive the peering fee that equalizes their net backbone transportation costs. We then consider fair cost sharing between an ISP and a transit provider. We derive the peering fee that equalizes their net backbone transportation costs, and illustrate how it depends on the traffic ratio and the amount of localization of that content. Finally, we consider the fair peering fee between an ISP and a content provider. We derive the peering fee that results in the same net cost to the ISP, and illustrate how the peering fee depends on the number of interconnection points and the amount of localization of that content. We dispense with the ISP argument that it should be paid regardless of the amount of localization of content.

cs.NI↗

arXiv2022

PoliGraph: Automated Privacy Policy Analysis using Knowledge Graphs (Journal Version)

Privacy policies disclose how an organization collects and handles personal information. Recent work has made progress in leveraging natural language processing (NLP) to automate privacy policy analysis and extract data collection statements from different sentences, considered in isolation from each other. In this paper, we view and analyze, for the first time, the entire text of a privacy policy in an integrated way. In terms of methodology: (1) we define PoliGraph, a type of knowledge graph that captures statements in a policy as relations between different parts of the text; and (2) we revisit the notion of ontologies, previously defined in heuristic ways, to capture subsumption relations between terms. We make a clear distinction between local and global ontologies to capture the context of individual policies, application domains, and privacy laws. We develop PoliGrapher, an NLP tool to automatically extract PoliGraph from the text using linguistic analysis. Using a public dataset for evaluation, we show that PoliGrapher identifies 40% more collection statements than prior state-of-the-art, with 97% precision. In terms of applications, PoliGraph enables automated analysis of a corpus of policies and allows us to: (1) reveal common patterns in the texts across different policies, and (2) assess the correctness of the terms as defined within a policy. We also apply PoliGraph to: (3) detect contradictions in a policy, where we show false alarms by prior work, and (4) analyze the consistency of policies and network traffic, where we identify significantly more clear disclosures than prior work. Finally, leveraging the capabilities of the emerging large language models (LLMs), we also present PoliGrapher-LM, a tool that uses LLM prompting instead of NLP linguistic analysis, to extract PoliGraph from the policy text, and we show that it further improves coverage.

cs.CR↗

arXiv2022

Goal-Space Planning with Subgoal Models

This paper investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Background planning with learned models is often worse than model-free alternatives, such as Double DQN, even though the former uses significantly more memory and computation. The fundamental problem is that learned models can be inaccurate and often generate invalid states, especially when iterated many steps. In this paper, we avoid this limitation by constraining background planning to a set of (abstract) subgoals and learning only local, subgoal-conditioned models. This goal-space planning (GSP) approach is more computationally efficient, naturally incorporates temporal abstraction for faster long-horizon planning and avoids learning the transition dynamics entirely. We show that our GSP algorithm can propagate value from an abstract space in a manner that helps a variety of base learners learn significantly faster in different domains.

cs.LG↗

arXiv2021

VICEROY: GDPR-/CCPA-compliant Enforcement of Verifiable Accountless Consumer Requests

Recent data protection regulations (such as GDPR and CCPA) grant consumers various rights, including the right to access, modify or delete any personal information collected about them (and retained) by a service provider. To exercise these rights, one must submit a verifiable consumer request proving that the collected data indeed pertains to them. This action is straightforward for consumers with active accounts with a service provider at the time of data collection, since they can use standard (e.g., password-based) means of authentication to validate their requests. However, a major conundrum arises from the need to support consumers without accounts to exercise their rights. To this end, some service providers began requiring such accountless consumers to reveal and prove their identities (e.g., using government-issued documents, utility bills, or credit card numbers) as part of issuing a verifiable consumer request. While understandable as a short-term cure, this approach is cumbersome and expensive for service providers as well as privacy-invasive for consumers. Consequently, there is a strong need to provide better means of authenticating requests from accountless consumers. To achieve this, we propose VICEROY, a privacy-preserving and scalable framework for producing proofs of data ownership, which form a basis for verifiable consumer requests. Building upon existing web techniques and features, VICEROY allows accountless consumers to interact with service providers, and later prove that they are the same person in a privacy-preserving manner, while requiring minimal changes for both parties. We design and implement VICEROY with emphasis on security/privacy, deployability and usability. We also thoroughly assess its practicality via extensive experiments.

cs.CR↗

arXiv2019

Soft Options Critic

The option-critic architecture (Bacon, Harb, and Precup 2017) and several variants have successfully demonstrated the use of the options framework proposed by Sutton et al (Sutton, Precup, and Singh1999) to scale learning and planning in hierarchical tasks. Although most of these frameworks use entropy as a regularizer to improve exploration, they do not maximize entropy along with returns at every time step. (Haarnoja et al., 2018d) recently introduced an off-policy actor critic algorithm in theSoft Actor Critic paper that maximize returns while maximizing entropy in a constrained manner thus enabling learning of robust options in continuous and discrete action spaces In this paper we adopt the architecture of soft-actor critic to investigate the effect of maximizing entropy of each options and inter-option policy in options framework. We derive the soft options improvement theorem and propose a novel soft-options framework to incorporate maximization of entropy of actions and options in a constrained manner. Our experiments show that the modified options-critic framework generates robust policies which allows fast recovery when environment is subjected to perturbations and outperforms vanilla options-critic framework in most hierarchical tasks

cs.AI↗

arXiv2019

Learning Action Representations for Reinforcement Learning

Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the structure is provided a priori. We show how a policy can be decomposed into a component that acts in a low-dimensional space of action representations and a component that transforms these representations into actual actions. These representations improve generalization over large, finite action sets by allowing the agent to infer the outcomes of actions similar to actions already taken. We provide an algorithm to both learn and use action representations and provide conditions for its convergence. The efficacy of the proposed method is demonstrated on large-scale real-world problems.

cs.LG↗