Time-Bucketed Balance Records: Bounded-Storage Ephemeral Tokens for Resource-Constrained Systems
Shaun Scovil, Bhargav Chickmagalur Nanjundappa

TL;DR
This paper introduces time-bucketed balance records, a storage-efficient data structure for ephemeral tokens that guarantees token expiration semantics while preventing storage growth and denial-of-service attacks in resource-constrained systems.
Contribution
It proposes a novel time-bucketing approach that bounds storage to O(k) per account, ensuring token expiration and security properties, with a practical Solidity implementation.
Findings
Storage is bounded by k+1 records per account.
Tokens never expire before their configured TTL.
Adversaries cannot increase victim's operation cost beyond O(k) amortized.
Abstract
Fungible tokens with time-to-live (TTL) semantics require tracking individual expiration times for each deposited unit. A naive implementation creates a new balance record per deposit, leading to unbounded storage growth and vulnerability to denial-of-service attacks. We present time-bucketed balance records, a data structure that bounds storage to O(k) records per account while guaranteeing that tokens never expire before their configured TTL. Our approach discretizes time into k buckets, coalescing deposits within the same bucket to limit unique expiration timestamps. We prove three key properties: (1) storage is bounded by k+1 records regardless of deposit frequency, (2) actual expiration time is always at least the configured TTL, and (3) adversaries cannot increase a victim's operation cost beyond O(k)[amortized] worst case. We provide a reference implementation in Solidity with…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSecurity and Verification in Computing · Distributed systems and fault tolerance · Data Quality and Management
