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arxiv: 2605.02886 · v1 · submitted 2026-05-04 · 💻 cs.OS

Recognition: unknown

CityOS: Privacy Architecture for Urban Sensing

Giorgio Cavicchioli , Mark Chen , Navid Salami Pargoo , Shuren Xia , Xiaotian Zhou , Roxana Geambasu , Jason Nieh , Jorge Ortiz
Authors on Pith no claims yet

Pith reviewed 2026-05-08 01:32 UTC · model grok-4.3

classification 💻 cs.OS
keywords urban sensingprivacy architectureoperating systemdifferential privacyedge computingsmart citiessensor data accessdata aggregation
0
0 comments X

The pith

CityOS mediates urban sensor data access through a three-tier privacy API for untrusted applications.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces CityOS as an operating system designed to manage how applications interact with urban sensing infrastructure such as cameras and environmental sensors. It addresses the lack of consistent privacy governance by using a three-tier API that allows increasing spatial scope of data use while applying stronger privacy measures at each level. The first tier keeps raw data local for real-time sensing, the second provides differentially private statistics for a single location, and the third enables citywide analysis with per-user privacy budgets enforced on devices. This approach runs applications in secure edge containers and broadcasts privacy loss information to promote transparency, allowing practical applications like traffic monitoring and safety alerts while aiming to protect individual privacy in public spaces.

Core claim

We present CityOS, an operating system for urban sensing that mediates application access to sensor data through a three-tier API inspired by structured, privacy-conscious web interfaces. The tiers expand the spatial scope of data access while imposing progressively stronger privacy constraints: On-Scene supports real-time sensing with raw data confined to the local context; Single-Locality Aggregation enables differentially private longitudinal statistics at a fixed location; and Cross-Locality Aggregation supports citywide analytics via aggregation across locations, with user devices enforcing per-user privacy budgets. CityOS runs as an edge runtime that executes untrusted applications in

What carries the argument

The three-tier API that progressively expands data access scope while strengthening privacy constraints, enforced by an edge runtime using ephemeral containers.

If this is right

  • Real-time sensing applications can operate with raw data kept local to the scene.
  • Longitudinal statistics at individual locations can be released under differential privacy guarantees.
  • Citywide analytics become feasible through cross-location aggregation while respecting individual privacy budgets enforced locally.
  • Applications run safely in ephemeral edge containers without persistent access to raw sensor streams.
  • Transparency is maintained by broadcasting differential privacy loss to users and operators.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Such an architecture could allow cities to expand sensor deployments with reduced fear of privacy violations.
  • Developers might create more innovative urban apps by relying on the standardized privacy tiers rather than custom solutions.
  • Integration with personal devices for budget enforcement suggests a hybrid model where users control their data contributions across the city network.

Load-bearing premise

Untrusted applications can be safely executed in ephemeral edge containers while reliably enforcing the three-tier privacy policies and differential privacy budgets without introducing new vulnerabilities or unacceptable performance costs in real-world urban deployments.

What would settle it

A demonstration that a malicious application running in the edge container can extract or leak raw sensor data beyond the allowed tier, or that the system cannot maintain acceptable performance under city-scale load, would disprove the architecture's viability.

Figures

Figures reproduced from arXiv: 2605.02886 by Giorgio Cavicchioli, Jason Nieh, Jorge Ortiz, Mark Chen, Navid Salami Pargoo, Roxana Geambasu, Shuren Xia, Xiaotian Zhou.

Figure 1
Figure 1. Figure 1: Deployment model. sensing and computation occur at many distributed locations (e.g., intersections, parking lots, transit stops, or retail spaces) view at source ↗
Figure 2
Figure 2. Figure 2: (a) CityOS architecture; (b), (c) running-example applications. view at source ↗
Figure 3
Figure 3. Figure 3: Detailed design. (a) API 1 (On-Scene) architecture. (b) API 2 (Single-Locality Aggregation) architecture. API 3 (Cross-Locality Aggregation) is illustrated in view at source ↗
Figure 4
Figure 4. Figure 4: API 3: Cross-Locality Aggregation. Key concepts: device-mediated federated measurement, on-device IDP enforcement, and node-user coordi￾nation for context-aware participation. per-TC budgets that bound total privacy loss per individual. Device-side privacy accounting. Shorter TCs better align with our limited-tracking principle and allow more frequent releases, while longer TCs provide a more meaningful pr… view at source ↗
Figure 5
Figure 5. Figure 5: CityOS application workflows. (a) Pedestrian safety (API 1). (b) Parking app (API 1+API 2). Methodology details (hardware configuration, dataset pre￾processing, noise parameters) are in §D. 6.1 API 1 On-Scene API 1’s main knob is maxEC, the ephemeral context’s lifetime. Shorter ECs reduce what a nearby sniffer can recover from any one location, but they also increase container-rotation overhead. We therefo… view at source ↗
Figure 6
Figure 6. Figure 6: Evaluation of CityOS APIs. (a) API 1 sniffing-attack effectiveness under time-scoped, localized outputs. (b) API 2 empirical sensitivity study. (c) API 2 DP counting accuracy (trusted vs. untrusted). (d) API 3 subway origin-destination (OD) accuracy vs. per-device budget. 0.0 0.5 1.0 1.5 2.0 Noise level (× feature std) 0.0 0.2 0.4 0.6 0.8 1.0 Score Accuracy F1 (macro) (a) Phase 1 IMU activity classificatio… view at source ↗
Figure 7
Figure 7. Figure 7: API 3 self-identification evaluation. (a)–(c) Accuracy of our two-stage self-identification pipeline. (d) Impact of identification errors on subway OD. plication from §5. We answer the first question here and the second in the subsequent section. We use the Hangzhou Metro smart-card dataset (29.2 M trips by 4.97 M riders across 80 stations over 25 days). Each entry event is stored on-device, and each exit … view at source ↗
Figure 8
Figure 8. Figure 8: API 1 performance overhead vs. maxEC on a desktop 5060 Ti and an edge Jetson AGX Orin. (a) Percentage of input frames dropped during container rotation. (b) Effective throughput in frames per second. The 5060 Ti reaches near-zero drops and 30 FPS by maxEC≥10 s; the Jetson plateaus near ∼17% drop / ∼25 FPS due to its slower GPU. Each data point runs for 5×maxEC to ensure at least five rotation cycles. D.1 A… view at source ↗
read the original abstract

Cities are rapidly deploying sensing infrastructure -- cameras, environmental sensors, and connected kiosks -- that continuously observe public spaces, yet they lack a system architecture governing how applications access, aggregate, and retain this data, creating privacy risks and preventing consistent policy enforcement. We present CityOS, an operating system for urban sensing that mediates application access to sensor data through a three-tier API inspired by structured, privacy-conscious web interfaces. The tiers expand the spatial scope of data access while imposing progressively stronger privacy constraints: On-Scene supports real-time sensing with raw data confined to the local context; Single-Locality Aggregation enables differentially private longitudinal statistics at a fixed location; and Cross-Locality Aggregation supports citywide analytics via aggregation across locations, with user devices enforcing per-user privacy budgets. CityOS runs as an edge runtime that executes untrusted applications in ephemeral containers, enforcing these policies and providing transparency via broadcasts of differential privacy loss. We implement CityOS and applications across all tiers -- including pedestrian safety alerts, real-time and forecast parking availability, traffic dashboards, and subway trajectory measurement -- and show that it supports practical streetscape applications while enforcing strong privacy.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper presents CityOS, an operating system for urban sensing that mediates application access to sensor data via a three-tier API: On-Scene (real-time raw data confined locally), Single-Locality Aggregation (differentially private longitudinal statistics at fixed locations), and Cross-Locality Aggregation (citywide analytics with per-user DP budgets enforced on devices). It describes an edge runtime executing untrusted apps in ephemeral containers, with policy enforcement and broadcasts of DP loss, and claims to implement example applications including pedestrian safety alerts, parking availability, traffic dashboards, and subway trajectory measurement that demonstrate practical support for streetscape apps while enforcing strong privacy.

Significance. If the enforcement and isolation claims are substantiated, CityOS would offer a novel structured architecture for privacy-preserving urban sensing, filling a gap in consistent policy enforcement for public infrastructure data and enabling safer aggregation across scales.

major comments (3)
  1. Abstract: The central claim that CityOS 'supports practical streetscape applications while enforcing strong privacy' rests on implementation descriptions, yet the manuscript supplies no quantitative results, performance metrics, accuracy/error analysis for the DP mechanisms, or measured privacy loss in the example applications.
  2. Edge runtime and container model: The architecture asserts that ephemeral containers isolate untrusted applications to prevent access to raw sensor data outside declared tiers or exceed per-user DP budgets, but provides no formal isolation argument, threat model, adversarial analysis, or verification results on side-channel or API-bypass risks.
  3. Cross-Locality Aggregation tier: The claim that user devices enforce per-user privacy budgets for citywide queries lacks details on budget tracking, composition across queries, or how the runtime prevents budget exhaustion or leakage in the presence of untrusted apps.
minor comments (2)
  1. The manuscript would benefit from explicit section headings or a dedicated evaluation section to separate architecture description from any empirical claims.
  2. Notation for the three tiers and DP parameters could be standardized with a table or diagram for clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We provide point-by-point responses to the major comments below and outline the revisions we plan to make.

read point-by-point responses

  1. Referee: Abstract: The central claim that CityOS 'supports practical streetscape applications while enforcing strong privacy' rests on implementation descriptions, yet the manuscript supplies no quantitative results, performance metrics, accuracy/error analysis for the DP mechanisms, or measured privacy loss in the example applications.

    Authors: We agree that the manuscript's evaluation is primarily based on implementation descriptions and example applications rather than quantitative metrics. The current version demonstrates the architecture through working prototypes but does not include performance benchmarks, DP accuracy analyses, or measured privacy losses. We will revise the abstract to more accurately reflect the scope of the evaluation and add a new section discussing the limitations of the current evaluation, including plans for future quantitative assessments. This addresses the concern without overstating the current results. revision: partial

  2. Referee: Edge runtime and container model: The architecture asserts that ephemeral containers isolate untrusted applications to prevent access to raw sensor data outside declared tiers or exceed per-user DP budgets, but provides no formal isolation argument, threat model, adversarial analysis, or verification results on side-channel or API-bypass risks.

    Authors: The paper relies on standard ephemeral container isolation provided by the underlying OS for separating untrusted apps from raw data and enforcing policies. However, we acknowledge the absence of a formal threat model or adversarial analysis in the manuscript. We will add a threat model section that specifies the assumptions (e.g., trusted runtime and hardware, untrusted applications) and discuss potential risks such as side-channel attacks, along with mitigation strategies employed. While a full formal verification is beyond the scope of this work, this addition will clarify the security claims. revision: yes

  3. Referee: Cross-Locality Aggregation tier: The claim that user devices enforce per-user privacy budgets for citywide queries lacks details on budget tracking, composition across queries, or how the runtime prevents budget exhaustion or leakage in the presence of untrusted apps.

    Authors: We will provide additional details in the revised manuscript on the budget tracking mechanism, which uses a per-user privacy budget ledger maintained by the trusted edge runtime. Composition is handled via the standard sequential composition property of differential privacy, with the runtime checking the remaining budget before executing any cross-locality query. To prevent leakage or exhaustion by untrusted apps, the enforcement occurs in the trusted component before any data is released to the app container. We will include a diagram and pseudocode to illustrate this process. revision: yes

Circularity Check

0 steps flagged

No circularity: system design paper with no derivations or self-referential reductions.

full rationale

The paper describes a three-tier API architecture for privacy in urban sensing, implemented via edge containers and differential privacy broadcasts. No equations, fitted parameters, predictions, or derivation chains exist that could reduce to inputs by construction. Claims rest on design choices and implementation examples rather than any self-defined or self-cited logical loop. This is a standard self-contained systems paper with no load-bearing self-citations or ansatzes that create circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The design rests on standard assumptions about differential privacy providing usable guarantees and container isolation being sufficient for policy enforcement; no free parameters, new entities, or ad-hoc axioms are introduced beyond domain-standard ones.

axioms (2)
  • domain assumption Differential privacy mechanisms can be composed and enforced across tiers while preserving utility for the described analytics applications
    Invoked for the Single-Locality and Cross-Locality Aggregation tiers.
  • domain assumption Ephemeral containers on edge devices can isolate untrusted code sufficiently to prevent policy bypass
    Central to the runtime enforcement claim.

pith-pipeline@v0.9.0 · 5519 in / 1381 out tokens · 68903 ms · 2026-05-08T01:32:31.569120+00:00 · methodology

discussion (0)

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