Post-Mortem Data Management Principles

• Post-mortem data management principles are a framework that governs the ethical stewardship, preservation, deletion, and secondary use of digital assets after an owner's death or dataset retirement.
• They integrate metadata and provenance to ensure reproducibility by reconstructing workflows and validating analytical pipelines through standardized schemas and graph models.
• These principles address privacy, ownership, and regulatory challenges while enabling machine unlearning and digital legacy planning in AI and social media contexts.

Post-mortem data management principles govern the stewardship, retention, deletion, and secondary use of digital assets and scientific records after the original data owner’s death or the retirement of datasets. These principles are critical for ensuring ethical, reproducible, and purposeful handling of archived or legacy data in contexts ranging from large-scale scientific workflows to digital legacies of individuals and AI training corpora.

1. Conceptual Foundations: Metadata and Provenance in Post-Mortem Contexts

Metadata is defined as “data about data,” providing structured information both about the logical meaning and physical details of datasets, including file names, origin, format, version, and user annotations (Deelman et al., 2010). Provenance refers to the process documentation specifying how data products have been derived, including all input datasets, applied parameters, transformation steps, software, hardware, and relevant environmental factors.

In post-mortem management, these records facilitate reconstruction of analytical pipelines, validation of results, and regeneration of intermediate products even when data is archived or the execution environment is unavailable. Provenance is frequently represented as a directed acyclic graph (DAG), establishing causal relationships between artifacts and processes (e.g., Artifact $A \xleftarrow[]{\text{wasGeneratedBy}}$ Process $P$).

2. Data Lifecycle Integration and Legacy Stewardship

The data lifecycle—comprising discovery, processing, replication, and archiving—positions metadata and provenance as an infrastructural backbone for post-mortem management (Deelman et al., 2010). Records generated during all phases are preserved in catalogs and archives, allowing reassessment and verification post-retirement.

Post-mortem management encompasses:

• The retention of metadata and provenance for future reproducibility;
• Reconstruction of workflows for validation or regeneration of data products;
• Ensuring lineage and correctness in the absence of original creators.

Challenges include determining what information is preserved, maintaining consistency across distributed systems, and managing evolving metadata versus static process provenance. Unified metadata and provenance systems adhering to common standards (e.g., Open Provenance Model) are recommended for robust legacy stewardship.

3. Technical Management Approaches and System Architectures

Metadata management utilizes standardized schemas (e.g., Dublin Core), layered descriptions (primary and secondary), controlled vocabularies, and ontologies (lightweight or heavyweight using RDF Schema/OWL) (Deelman et al., 2010). Provenance capture is implemented via integration with workflow management systems (Pegasus, Kepler, VisTrails), using either:

• Integrated environments (tight coupling of workflow and provenance capture);
• Autonomous stores (independent storage and query interfaces; e.g., PASOA, Karma).

Provenance graphs formalize causal dependencies, enabling formal reasoning; e.g., $\forall A, P: A = f(P)$ when artifact $A$ is generated by process $P$. Storage technologies span relational databases, XML databases, and RDF triple stores for structured query and semantic inference.

Signature-based event reconstruction in forensic analysis automates inference of high-level user actions from collections of low-level traces and timestamps (James et al., 2013). Event detection algorithms rely on the consistency of timestamp updates across core traces. Formally, given timestamps $t_1, \ldots, t_n$, the event occurs if $\forall i, j,\ |t_i - t_j| \leq \Delta$, with $\Delta$ a defined threshold (typically $\leq 1$ minute).

Provenance and metadata management in collaborative analysis workflows employ property graph models, version graphs, snapshot tracking, and derivation records, supported by technologies like git and Neo4j for efficient querying, introspection, and monitoring (Miao et al., 2016).

4. Post-Mortem User Data: Privacy, Ownership, and Platform Practices

The management of deceased user data on social platforms presents regulatory, ethical, and practical complexities. Empirical studies show users desire granular control over their post-mortem digital footprint, preferring trusted individuals or secure third-party solutions over default platform stewardship (Reeves et al., 1 Jul 2024). Social media companies’ legacy contacts or account managers typically offer memorialization or deletion, with limited granularity and low trust.

From a privacy perspective, death represents a transition point: privacy expectations set during life may not persist post-mortem, creating tensions between security, privacy, and sharing intentions (Holt et al., 2021). This “post-mortem privacy paradox” manifests in users valuing planning without active engagement, constrained by conflicts between robust security (e.g., password managers, MFA) and nuanced sharing after death.

Design recommendations include:

• Granular access controls for legacy tools;
• Continuous review/update capabilities;
• Supported transfer of secondary authentication material;
• Automated actions (scheduled deletions, asset transfers).

Region-specific research demonstrates that behavioral predictors such as attitude, subjective norms, and perceived behavioral control significantly influence management intentions (Young et al., 25 Feb 2025). The formal PLS-SEM model is $$INT = \beta_{att}\cdot ATT + \beta_{sn}\cdot SN + \beta_{pbc}\cdot PBC + \epsilon$$, with coefficients reflecting the strength of each predictor.

5. Principles for Post-Mortem Data Management in Generative AI Systems

Recent work articulates dedicated principles for post-mortem management within generative AI frameworks (Jarin et al., 9 Sep 2025):

• Right to be Forgotten/Data Deletion: Deceased individuals should be able to erase their personal data, including its influence on trained models. Upon verified death, systems must remove raw data and apply machine unlearning (e.g., via SISA or MUSE techniques), ensuring updated models $M’$ are independent of deleted data $d$. Notationally: $$\text{Delete}(u) = \{d \in D \mid \text{data from } u\}$$ and update $M$ so $M'$ is independent of $d$.
• Data Inheritance and Ownership: Individuals may opt to transfer or monetize their digital legacy, via data bequeathal, raw asset transfer, or economic rights. Implemented through digital will mechanisms and cryptographic controls (attribute-based encryption, homomorphic signatures).
• Purpose Limits and Harm Prevention: If post-mortem data is used for research or societal benefit, explicit purpose limitations, transparency, and safeguards must be enacted. Technical measures such as watermarking, canary injection, and differential privacy apply. The latter is expressed: $$\Pr[\mathcal{M}(D) \in S] \leq e^\epsilon \Pr[\mathcal{M}(D') \in S] + \delta,$$ indicating output indistinguishability after data change.

Regulatory gaps persist: most privacy laws (GDPR, CCPA, LGPD) emphasize the rights of living users. Post-mortem management on major platforms is limited to account memorialization and does not address broader risks of generative AI, such as unauthorized digital cloning or misuse.

6. Operational Recommendations and Future Research Directions

Expert consensus underscores regulatory and technical interventions:

• AI privacy policies should specify post-mortem data management, enforce deletion within standardized intervals, and support digital will frameworks akin to estate law (Jarin et al., 9 Sep 2025).
• Prohibitions on post-mortem data use for targeted advertising, political manipulation, and deepfake generation are recommended.
• Technical designs should integrate privacy and safety by design, machine unlearning, differential privacy, watermarking, and auditability.
• Third-party solutions interfacing with major vendors are needed for unified execution of digital wills (Reeves et al., 1 Jul 2024).
• Empirical audits to measure memorization in Gen-AI, the effectiveness of machine unlearning, and secure economic valuation models are future research priorities.

Effective post-mortem data management requires cross-disciplinary collaboration, standardized frameworks, and sustained attention to ethical stewardship, reproducibility, privacy, and user autonomy in both scientific and consumer digital ecosystems.