Forensic Intelligence Review · Budding Forensic Expert

The Forensic Shadow

How much of a person's life can be reconstructed from data they never knew they created?

How investigators reconstruct movement, association and routine from passive, machine-generated traces — and where that reconstruction stops being evidence.

Fig. 1 — No single system observes the whole person. Each records only its own fragment of an ordinary afternoon.

An investigator may never recover a suspect's phone. Yet a vehicle logs a Bluetooth pairing event, a Wi-Fi access point logs an association, a cloud account logs a synchronisation, a payment switch logs a transaction, and a neighbour's smart doorbell logs a passing shape. None of these records exists because the person decided to leave one. Each is a by-product of a machine doing its ordinary job. This is the forensic shadow — the distributed, largely involuntary trace that infrastructure, sensors and platforms leave around a person's presence, as opposed to the trace a person consciously chooses to create.

The claim of this review is two-sided. Passive traces — device telemetry, network associations, payment metadata, IoT and wearable logs, vehicle infotainment data, cloud synchronisation events — can, when genuinely independent and properly attributed, support increasingly fine-grained inference about where a device was and what its likely user was doing. Human mobility research shows individual trajectories are both highly unique (four spatio-temporal points identify the large majority of individuals in a mobility dataset) and highly regular (entropy analyses put predictability above 90% for most people) — a combination that makes passive traces forensically potent (de Montjoye et al., 2013; Song et al., 2010).

But evidential value does not travel automatically with existence. Between "a record exists" and "a person did something" lies a chain of inferential steps — device association, account attribution, identity resolution, physical presence, human activity, behavioural pattern, life narrative — and each step is a place where error can enter. The article develops two original frameworks, the Forensic Shadow Reconstruction Model and the Forensic Shadow Confidence Gradient, and introduces the concept of forensic narrative drift: the risk that a reconstruction built from individually authentic records can still be collectively wrong.

Key Findings at a Glance

Human mobility traces are both highly unique and highly predictable — why passive location-adjacent data is forensically potent, and why it re-identifies people even when "anonymised" (de Montjoye et al., 2013; Song et al., 2010; Farzanehfar et al., 2021).
IoT devices are engineered to be passive and low-visibility; simply detecting that a data source exists is often the first forensic challenge (Stoyanova et al., 2020).
Bluetooth proximity — a common basis for "co-location" claims — has documented accuracy limits; commodity ranging has been reported below roughly 60% accuracy under real conditions (Leith & Farrell, 2020).
Timestamps across systems are not automatically comparable; clock skew and drift are well-documented sources of false temporal ordering (Boyd & Forster, 2004; Boztas et al., 2024).
Cloud forensics research flags jurisdiction, multi-tenancy and provenance as unresolved structural problems — reconstructions are bounded by legal access, not only technical capability (Ab Rahman et al., 2019; Pichan et al., 2015).
Entity resolution research shows that linking records to "one person" is a probabilistic exercise with a known error surface, not a deterministic lookup (Binette & Steorts, 2022).
NIST guidance states plainly that every forensic result carries uncertainty — historically under-quantified across disciplines (NIST, 2025; Swofford et al., 2024).
India's UPI ecosystem shows the phenomenon at national scale: RBI reported transaction value up 137% over two years to roughly ₹200 trillion, alongside a fivefold rise in reported digital-payment fraud value.

Digital forensics has historically organised itself around records a person chose to make: a message sent, a photograph taken, a document authored. The evidentiary logic was direct — a human act produced an artefact, and the artefact could, subject to authentication, be traced back to the act.

That framing is now incomplete. A growing share of the data surrounding a person's life is generated not by their decision to record something, but by the ordinary operation of systems they did not design and often never configure: a router logging an association, a payment switch logging a settlement, a smart speaker logging a wake-word event, a car logging a Bluetooth handshake. None of these events required the person to intend evidentiary production. Each is a side effect.

Central Paradox

Can a person leave forensic evidence without consciously creating evidence? IoT and passive-device forensics answers, cautiously, yes — these systems generate genuine, forensically valuable artefacts — but passive generation changes the evidential character of the trace. A message says "I wrote this." A Bluetooth association event says only that a device matching this identifier was, at some point, within radio range, under conditions the receiver's firmware judged sufficient to log.

Proposed Definition

The Forensic Shadow: a distributed set of technological traces generated directly, indirectly, or automatically through an individual's interaction with devices, networks, accounts, infrastructure, and sensor environments, from which aspects of identity, activity, association, or behaviour may potentially be reconstructed.

This is offered as a proposed conceptual definition for forensic discussion, drawing on established distinctions between digital footprint, metadata, and passive versus active data generation in the IoT-forensics literature (Woodward et al., 2007; Servida & Casey, 2019). It is useful to separate several concepts the phrase "digital footprint" tends to blur:

Digital footprint — the general, user-facing accumulation of online activity.
Digital trace — any discrete record left by a technical event.
Metadata — data describing other data.
Passive data — generated automatically by device or infrastructure operation.
Sensor data — the output of a physical measurement.
Behavioural data — patterns abstracted from repeated technical events over time.
Forensic intelligence — traces that have been evaluated, corroborated, and integrated into an investigative hypothesis.

The forensic shadow is the union of these categories as they apply to one individual — but the union of traces is not the same thing as a reconstructed life. That gap is this article's organising problem.

Intentionally generated records — messages, photographs, posts — carry an implicit assertion of authorship: a person acted in order to produce this artefact. Passive traces carry no such assertion. A device-association log, a synchronisation timestamp, or a proximity event records only that a technical process occurred, not that a person decided to act.

Does passive generation weaken the evidential connection to conscious human activity? IoT-forensics research suggests the answer is often yes: devices are frequently "always connected... accessible from practically anywhere," and many are engineered to remain low-visibility, "designed to work passively and autonomously" (Kebande & Ray, 2016; Stoyanova et al., 2020). Passive traces are also frequently produced by background processes — synchronisation, telemetry, health checks — meaning a well-populated log can exist for a period in which the human user did nothing observable whatsoever.

A single event today can leave fragments across smartphones, cloud back-ends, home Wi-Fi routers, Bluetooth accessory ecosystems, connected vehicles, wearables, payment rails, telecom infrastructure, CCTV systems, smart-home hubs, and general-purpose IoT devices. The forensic record of an event is, accordingly, geographically, technically, and institutionally distributed — a point made repeatedly in the cloud- and IoT-forensics literature, where jurisdiction, multi-tenancy, proprietary formats, and device heterogeneity are first-order obstacles, not secondary details (Pichan et al., 2015; Ab Rahman et al., 2019; Stoyanova et al., 2020).

This distribution has direct consequences for four practical stages of forensic work:

Acquisition — no single seizure captures the whole shadow; investigators must typically request records from multiple, independently governed custodians.
Correlation — combining records requires resolving differences in timestamp format, time zone handling, identifier scheme, and logging granularity before any comparison is meaningful.
Jurisdiction — cloud and platform data frequently reside outside the investigating authority's territory, and cross-border cooperation is slow relative to data volatility.
Data loss — retention windows and device-side deletion mean the theoretical shadow and the acquirable shadow are different objects.

GPS is only one of several ways a device's location can be estimated. Wi-Fi association records reveal proximity to a known access point; cellular attachment reveals the serving cell; Bluetooth events reveal nearness to another known device; payment events geolocate a terminal at a known address; vehicle telematics record GNSS tracks independent of any phone. Combined, these sources can approximate location even where GPS was disabled or never queried.

Critical Distinction

The location of a device is not the location of a person. A phone's Wi-Fi association tells an investigator where a phone was, conditioned on the phone being on, in range, and set to auto-associate. Whether the owner was holding it, someone else was carrying it, or it was left behind, are separate questions the location trace alone cannot answer.

Bluetooth co-location, shared Wi-Fi networks, and repeated temporal-spatial overlap are frequently used to infer social association. The Bluetooth contact-tracing literature — tested at pandemic scale — is instructive: commodity Received Signal Strength Indicator (RSSI) is affected by device orientation, body attenuation, and manufacturer-specific radio behaviour, with distance-estimation accuracy reported below roughly 60% in practical deployments (Leith & Farrell, 2020; Chan, Bakshi & Rea, 2020). Bluetooth's nominal 10-metre range is frequently exceeded, with usable detection out to 100 metres under favourable conditions — far beyond any meaningful definition of "close contact."

Repeated technological proximity is therefore, at best, a weak, noisy proxy for social association — vulnerable to environmental coincidence, shared infrastructure artefacts, and signal propagation through walls and floors. Genuine social-association inference requires repeated, independently corroborated co-presence across varied contexts, not a single overlap event.

Repeated passive traces can reveal commuting patterns and temporal structure. This is well grounded: entropy-based analyses of large mobile-phone datasets found approximately 93% theoretical predictability in users' whereabouts, with a strong tendency to return to previously visited locations (Song et al., 2010). Related work on "eigenbehaviours" shows routine itself has a detectable, decomposable structure in passive data (Eagle & Pentland, 2009).

The interpretive risk is conflating a repeated technological pattern with a stable human habit. A recurring 9 a.m. weekday Wi-Fi association may reflect a fixed workplace — or a shared device left charging somewhere the person no longer visits, a background sync unrelated to presence, or a family member's overlapping schedule on a shared account.

Investigators frequently cannot recover a subject's primary device. IoT, cloud, and vehicle-forensics research collectively demonstrate that the absence of the primary device does not equate to the absence of digital evidence: cloud synchronisation, associated secondary devices, and vehicle infotainment caches can independently preserve fragments of the same activity (Chung et al., 2017; Kim et al., 2022).

This reconstruction is inherently partial, however. What survives is whatever other systems happened to log — not a substitute for the device's own record — making such reconstructions especially exposed to selection bias: the picture available is shaped by which systems kept records, not by which parts of the person's activity were most significant.

Data fusion — combining multiple individually weak or ambiguous evidence sources into a stronger composite inference — is well established, from multi-algorithmic image annotation fusion (Al Mashhadani et al., 2019) to Dempster-Shafer evidence-theory approaches in network forensics (Ye et al., 2015) to deep-learning fusion frameworks for investigative triage (Senthil & Selvakumar, 2022). The promise is genuine: independent weak signals, properly combined, can support a conclusion no single signal could support alone.

The hazard is equally established: multiple traces are not automatically independent. Fusion assumes some degree of statistical independence between sources. When several "sources" actually derive from one underlying event, fusion does not strengthen the evidence — it double-counts it while creating an illusion of strength.

Records that appear to originate from separate systems can trace back to a single underlying event: a location "confirmed" by both a cell-tower record and a weather-app notification that derived its own location from the same GPS fix; a payment "corroborated" by both a merchant terminal log and a bank settlement record that are simply two views of one transaction. Cloud-forensics research on data provenance stresses precisely this difficulty — establishing genuine, traceable origin as distinct from a duplicated copy remains one of the field's persistent open problems (Manral et al., 2019).

Working Rule

Corroboration is weakened, not strengthened, when multiple records merely repeat the same underlying event under different labels. Before treating several traces as mutually corroborating, ask whether they could plausibly share a common cause — a shared server, a duplicated database, a synchronisation cascade.

Digital forensics distinguishes several layers of identity frequently treated, incorrectly, as interchangeable: device identity (a specific piece of hardware), account identity (a login, possibly shared or multi-device), subscriber identity (the contractually registered party), user identity (whoever was actually operating the device at a given moment), and human identity (the specific person investigators need to name).

The entity-resolution literature formalises the technical version of this problem: linking records to "the same real-world entity" is a probabilistic matching exercise, not a deterministic lookup, and it carries a known, quantifiable error surface of false matches and false non-matches (Binette & Steorts, 2022; Christen, 2012). Moving from "this account generated this trace" to "this person performed this action" requires an explicit attribution argument, not an assumption.

Inferential Chain

Device event ≠ User action ≠ User intention ≠ Human behaviour.

A device event — a notification fetched, a beacon logged, an app woken by the operating system — can occur with no user interaction whatsoever. Even a genuine tap or gesture does not, by itself, establish why a person acted, and a single action rarely establishes a pattern. A trace may accurately represent a technical event while still failing to establish the human activity an investigator is tempted to infer from it — the trace is not wrong; the inferential leap built on top of it carries the risk.

Distributed data can support inference about association networks and co-location — social-network analysis of mobile-communication metadata has revealed structural properties of tie strength at population scale (Onnela et al., 2007). But there is a wide gap between detecting a structural pattern in metadata and inferring the social or legal meaning of a relationship. Metadata can show two identifiers interacted repeatedly; it cannot, alone, characterise the content, context, or significance of that interaction.

Sequencing events correctly is foundational to any narrative reconstruction, and timestamps are the primary tool. But timestamp research shows this foundation is less solid than it appears. Clock skew (a persistent offset from true time) and clock drift (gradual, cumulative deviation) are well documented, arising from network delay or faulty synchronisation (Boyd & Forster, 2004; Buchholz & Tjaden, 2007). Recent work on "time anchors" formalises the problem: an examiner comparing a system timestamp to a real-world reference may find a skew of hours, not seconds (Boztas, Sadeghi & Van de Bunt, 2024).

Fig. 2 — Two systems' clocks can silently diverge; a correlated "sequence" built across them may reorder genuinely unrelated events.

Temporal correlation can establish a plausible sequence; it cannot, by itself, establish causation. Two events occurring in close succession across independently clocked systems may reflect genuine sequence, coincidence, or an artefact of clock error — only independent verification of clock reliability lets an investigator tell them apart.

What does it mean when an expected trace does not exist? The event genuinely never occurred; the system was configured not to log it; the record exceeded a retention window before acquisition; acquisition itself failed; or the record was deliberately deleted. These causes are easy to conflate. Absence of evidence is not evidence of absence — a principle with particular force in environments defined by heterogeneous retention policies and inconsistent logging defaults (Stoyanova et al., 2020).

The most demanding scenario in forensic reconstruction is not the corrupted or forged record — it is the case where every individual record is authentic, every extraction is technically correct, every timestamp is correctly read, yet the combined narrative is wrong. This can occur through misattribution, evidence dependency, coincidental correlation, missing context, or false temporal ordering.

Proposed Concept — Forensic Narrative Drift

The progressive movement from individual observations toward increasingly confident narratives through a chain of assumptions that may individually appear reasonable but collectively produce an unsupported reconstruction.

Its distinguishing feature: it does not require bad faith or negligence. It can emerge purely from the compounding of small, individually defensible inferential steps, each quietly narrowing the space of alternative explanations under consideration (drawing on Dror, 2020; National Research Council, 2009).

Genuine corroboration requires distinguishing five things often lumped together: repeated data (the same record in multiple extractions), duplicated data (a copy propagated by synchronisation), correlated data (records that co-vary, causally or not), independent evidence (genuinely separate processes with no shared upstream cause), and convergent evidence (independent evidence supporting the same conclusion). Only the last constitutes corroboration in the scientifically meaningful sense.

Sensors do not record "reality" directly; they record an engineered interpretation of a physical signal, filtered through sampling rate, calibration, and firmware logic built for battery life and user experience, not forensic accuracy. Bluetooth RSSI is illustrative: it is not a distance measurement, it is a signal-strength measurement a downstream algorithm converts into a distance estimate, with substantial documented error (Leith & Farrell, 2020). The forensic question is not simply "what did the sensor record," but "what pipeline produced this number, and what is its error profile."

The forensic shadow exists because ordinary infrastructure was built for operational and commercial purposes, not surveillance. Its forensic capability is, in this precise sense, a secondary use of data generated for something else. Research on re-identifiability of "anonymised" mobility data shows how far this extends: four spatio-temporal points can uniquely identify most individuals in a large dataset (de Montjoye et al., 2013), a finding shown to hold, with only modest degradation, even in country-scale datasets (Farzanehfar et al., 2021). The scientifically grounded framing is not that this capability is inherently illegitimate, but that technological ecosystems create forensic capability that substantially exceeds the purpose for which the data was originally generated.

Machine learning increases reconstruction capability chiefly through entity resolution at scale (Binette & Steorts, 2022), anomaly detection across heterogeneous datasets (Senthil & Selvakumar, 2022), and re-identification of nominally anonymised data through learned mobility signatures. These capabilities come with equally real limitations: automation bias, opaque inference, dataset bias, and the risk that AI-assisted correlation simply automates the production of a more fluent — not more validated — narrative. Validated, independently tested AI-assisted attribution methods remain a genuine research gap.

Reconstruction is bounded by missing data, representativeness (a log is not a representative sample of a whole life, only of moments a particular system happened to observe), attribution uncertainty, causal-inference limitations, technological bias, and infrastructure dependency. How much of a person can be reconstructed before the reconstruction becomes speculation does not have a fixed numerical answer — it is answered case by case, through the attribution, independence, and uncertainty tests developed throughout this review.

A conceptual framework proposed for forensic discussion based on the literature reviewed — not an established international forensic standard.

Fig. 3 — The Forensic Shadow Reconstruction Model. Width represents evidentiary certainty at each level.

Level	What Is Established	Necessary Corroboration
1 · Raw Trace	A technical record exists.	None yet — but provenance must be documented.
2 · System Association	The trace is linked to a device or system.	Device/system identifiers verified against known references.
3 · Entity Attribution	The trace is associated with an account or identity.	Account-to-person argument; rule out shared use/automation.
4 · Corroborated Activity	Independent traces support one technical/physical event.	Independence test — sources must not share a common cause.
5 · Behavioural Pattern	Repeated evidence supports a routine/habit inference.	Repetition across varied contexts; alternatives addressed.
6 · Investigative Reconstruction	Multiple validated inferences form a broader narrative.	Each level independently defensible; narrative drift checked.

A second original framework, proposed for discussion.

Fig. 4 — Confidence should not remain constant as interpretation moves away from the raw observation.

Confidence should not automatically remain constant as forensic interpretation moves further from the original data observation. Each downward step is an additional inferential transformation, and — absent explicit, independent validation at that step — uncertainty should compound, not hold steady. A narrative at the bottom of the gradient is not disqualified by this principle; it is required to carry, and disclose, the accumulated uncertainty of everything above it.

Trace Type	Directly Establishes	May Support	Attribution Risk	Interpretive Limitation
Device telemetry	A technical event occurred on the device	Device state at a given time	Background activity ≠ user action	May reflect automation, not human intent
Network association	Device was in range of an access point/cell	Approximate device location	Device presence ≠ owner presence	Range/accuracy varies by density & tech
Payment records	A transaction occurred at a terminal/time	Presence near a merchant location	Account holder ≠ transaction initiator	Shared cards, authorised users, remote init.
Cloud sync metadata	Data was uploaded/synced at a given time	Device activity window	Sync ≠ real-time user action	Delayed/batched sync distorts timing
Bluetooth proximity	Two devices were within radio range	Possible physical proximity	Radio proximity ≠ social contact	RSSI-to-distance error is substantial
IoT sensor logs	A sensor-defined event occurred	Occupancy or activity pattern	Sensor trigger ≠ identified individual	Multiple occupants, pets, false triggers
Vehicle telematics	Vehicle state / paired device / route	Driver/passenger presence, route history	Vehicle use ≠ single named driver	Shared vehicles, multiple paired devices
Wearable data	A physiological/activity signal on the wearer	Activity type, exertion, rough location	Device worn ≠ device owner active/aware	Accuracy varies by manufacturer

Data Fusion and Corroboration Table

Sources	Apparent Event	Potential Common Cause	Independence	Key Uncertainty
Phone GPS + weather app notification	Device at Location X	Weather app pulled same GPS fix	Not independent	Appears as two sources, is one
Merchant terminal + bank settlement log	Transaction at Time T	Both derive from one payment message	Not independent	Confirms detail, not who initiated it
Home Wi-Fi + smart-speaker wake event	Presence at home	Same physical arrival triggers both	Possibly independent	Depends on remote/automatic trigger risk
Cell attachment + separate CCTV footage	Presence at Location Y	Genuinely separate custodians	Independent	Requires clock-skew correction
Cloud-synced photo, two extractions	Photo existed	One is a copy of the other	Not independent	Easy to miscount as two exhibits

Validated methods for testing statistical independence between forensic sources prior to fusion.
Standardised frameworks for communicating cumulative uncertainty across multi-step inferential chains.
Cross-platform attribution models that formally handle shared devices and accounts.
Ground-truth datasets validating passive-trace-to-behaviour inference, not just trace existence.
Ex-ante clock-reliability certification standards for consumer IoT and vehicle systems.
Formal semantics for "passive" vs. "active" data generation across device architectures.
Black-box testing of AI-assisted entity resolution and fusion tools in investigative use.
Longitudinal re-identification-risk studies as mobility/transaction datasets scale further.
A standardised taxonomy for the "problem of absence" in forensic reporting.
Comparative legal-technical frameworks for cross-jurisdictional cloud evidence timeliness.
Empirical error-rate studies for Bluetooth/Wi-Fi proximity inference in forensic (not epidemiological) contexts.
Automated detection frameworks for "evidence dependency" in large multi-source case files.
Reproducibility standards for vehicle and wearable extraction across firmware versions.
Bias-auditing standards for AI-assisted behavioural-pattern detection.
Formal uncertainty-propagation models for Levels 4–6 of the Reconstruction Model.

Future Research Agenda

Priority 1 — Validated Multi-Source Forensic Data Fusion

Rationale Fusion is already in operational use; validation lags behind adoption.Difficulty High — needs representative, labelled multi-source datasets.Value Directly reduces narrative-drift risk.

Priority 2 — Evidence Independence Assessment

Rationale Dependency detection currently relies on analyst judgement, not formal method.Difficulty Moderate — provenance metadata is often available but underused.Value High, low-cost improvement to existing casework.

Priority 3 — Attribution Confidence Frameworks

Rationale Identity attribution is argued case-by-case with no shared standard.Difficulty Moderate.Value Improves consistency and appellate defensibility.

Priority 4 — Behavioural Inference Validation

Rationale Routine claims are grounded at population level, under-validated per case.Difficulty High.Value Addresses the reconstruction's weakest inferential step.

Priority 5 — Passive Data Ground Truth Datasets

Rationale Most datasets validate trace existence, not the behaviour built on it.Difficulty Very high, given privacy constraints.Value Foundational for every other priority.

Priority 6 — AI-Assisted Reconstruction Validation

Rationale Prevents automation bias from becoming institutionalised.Difficulty High — requires access to proprietary fusion tooling.Value Keeps AI-assisted conclusions falsifiable.

Priority 7 — Standardised Uncertainty Reporting

Rationale NIST already calls for this in traditional forensic disciplines.Difficulty Moderate — a training/reporting-practice problem, not a technical one.Value Highest value-to-implementation ratio of all seven priorities.

Conclusion

A phone is never found, and yet a car, a router, a bank, and a stranger's doorbell each hold a fragment of the same afternoon. Every one of those fragments is real. None of them, alone, is a biography.

Modern technological systems generate a genuinely distributed forensic shadow — passive, largely involuntary, and increasingly rich, because the devices, networks, and platforms that surround ordinary life were built to serve convenience and commerce, not evidentiary completeness, and yet they log almost everything anyway. Mobility research shows human movement is unique enough, and regular enough, that a handful of location-adjacent points can single a person out of a population of millions, and predict most of where they will be next. Cloud, IoT, vehicle, and wearable forensics have each shown that meaningful evidence survives even when the primary device does not.

But no individual trace should automatically graduate into a behavioural conclusion, and no accumulation of traces should automatically graduate into a life. Reconstruction depends on attribution that can withstand "could this be someone else, or no one, or the machine acting alone?" It depends on evidence independence that can withstand "are these really two observations, or one observation wearing two labels?" It depends on correlation that tests alternative explanations before settling on the most convenient one — and on carrying uncertainty forward rather than letting it quietly evaporate somewhere between the fourth exhibit and the closing argument.

The modern forensic challenge, in the end, is not primarily whether investigators can find traces of a person — increasingly, they nearly always can. It is knowing, at every step of the reconstruction, whether they are still reading evidence, or whether they have quietly begun writing a story the evidence was never asked to tell.

References (APA 7th Edition)

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A note on sourcing: this review draws on genuinely verifiable peer-reviewed and institutional sources (Nature, Science, Scientific Reports, ACM Computing Surveys, IEEE Communications Surveys & Tutorials, Digital Investigation, PNAS, NIST). It deliberately stops short of padding the reference list with unverifiable citations.

How much of a person's life can be reconstructed from data they never knew they created?

The Forensic Shadow

The Person Who Never Created the Record

Defining the Forensic Shadow

Intentional Data Versus Passive Data Generation

The Distributed Nature of Modern Evidence

Location Without GPS: Reconstructing Presence From Indirect Traces

Association and Proximity: When Does Nearness Become Evidence?

The Routine Reconstruction Problem

The Missing Device Problem

Data Fusion: When Weak Signals Become Stronger Together

The Evidence Dependency Problem

Identity Attribution: Whose Data Is It?

From Device Activity to Human Behaviour

The Forensic Reconstruction of Social Life

The Temporal Reconstruction of a Life

The Problem of Absence

False Reconstruction: When Real Data Produces a False Story

The Science of Corroboration

Machine-Generated Witnesses

Privacy, Surveillance and the Forensic Shadow