LeapYear solves today’s data-sharing challenges for the world’s most confidential information.

Modern data contains immense value. However, due to its sensitivity, extracting full value from data has never been harder. Your projects demand a new way to both leverage and protect sensitive data with Leapyear software.

Differential Privacy at Scale

Rigorous Protection, High Value Applications

Realizing data-driven economic advantage is table stakes for every company. But traditional approaches to protect sensitive data restrict innovative use of these prized assets. LeapYear protects confidential information with mathematically proven privacy, giving you the freedom to pursue high value applications with your most sensitive data.

leapyear

raw data

broad aggregate

business rules

Credit Card NetworkSecurities ServicesPrime BrokerageSpeciality PharmacyClaims ProcessingClearing HousesBankingFinancial ServicesHealthcareLEAPYEARMETHODProtect all confidential information while maximizing value. PROS:Small learning curve to understand and implement vs status quo. CONS:ACCURACYCOMPLETENESSPRIVACY RISKIMPACT OF METHOD ON DATASolutions are crafted for specific data types and use cases. LeapYear uses mathematically proven methods to protect data.LEAPYEAR
quo.implement vs statusto understand andSmall learning curve CONS:maximizing value. information whileProtect all confidential PROS:HealthcareFinancial ServicesBankingClearing HousesClaims ProcessingSpeciality PharmacyPrime BrokerageSecurities ServicesCredit Card NetworkPRIVACY RISKCOMPLETENESSACCURACYIMPACT OF METHOD ON DATALEAPYEARMETHODLEAPYEAR and use cases.Solutions are crafted for specific data types methods to protect data. LeapYear uses mathematically proven
Credit Card NetworkSecurities ServicesPrime BrokerageSpeciality PharmacyClaims ProcessingClearing HousesBankingFinancial ServicesHealthcareRAW DATAMETHODAbsolute maximum data value. PROS:Zero privacy protection. CONS:This method gives any analyst full access to data. Raw data provides access to row level information. RAW DATAACCURACYCOMPLETENESSPRIVACY RISKIMPACT OF METHOD ON DATA
protection. Zero privacy CONS:data value. Absolute maximum PROS:HealthcareFinancial ServicesBankingClearing HousesClaims ProcessingSpeciality PharmacyPrime BrokerageSecurities ServicesCredit Card NetworkPRIVACY RISKCOMPLETENESSACCURACYIMPACT OF METHOD ON DATARAW DATAMETHODRAW DATAdata.This method gives any analyst full access to information. Raw data provides access to row level
Credit Card NetworkSecurities ServicesPrime BrokerageSpeciality PharmacyClaims ProcessingClearing HousesBankingFinancial ServicesHealthcareAGGREGATEBROADMETHODProducts and solutions widely available. PROS:Removes significant data value. Thismethod can only protect fields explicitlypresent in the dataset. CONS:Techniques are often driven by historical best practices. These methods seek to protect privacy through aggregation of fields. BROAD AGGREGATEACCURACYCOMPLETENESSPRIVACY RISKIMPACT OF METHOD ON DATA
explicitly present in the dataset. can only protect fieldsdata value. This method Removes significant CONS:widely available.Products and solutions PROS:HealthcareFinancial ServicesBankingClearing HousesClaims ProcessingSpeciality PharmacyPrime BrokerageSecurities ServicesCredit Card NetworkPRIVACY RISKCOMPLETENESSACCURACYIMPACT OF METHOD ON DATABROAD AGGREGATEMETHODBROAD AGGREGATE best practices.Techniques are often driven by historical through aggregation of fields. These methods seek to protect privacy
Credit Card NetworkSecurities ServicesPrime BrokerageSpeciality PharmacyClaims ProcessingClearing HousesBankingFinancial ServicesHealthcareBUSINESSRULES METHODSimple to understand and enforcein practice. PROS:Techniques remove information orcompletely block data release and are easily reverse engineered. CONS:These rules are often situationally based, using historical best practices. Business rules are applied to enforce conditions to approve/deny use of data. BUSINESS RULESACCURACYCOMPLETENESSPRIVACY RISKIMPACT OF METHOD ON DATA
release and are easily reverse engineered. completely block datainformation orTechniques removeCONS:and enforce in practice. Simple to understand PROS:HealthcareFinancial ServicesBankingClearing HousesClaims ProcessingSpeciality PharmacyPrime BrokerageSecurities ServicesCredit Card NetworkPRIVACY RISKCOMPLETENESSACCURACYIMPACT OF METHOD ON DATABUSINESS RULES METHODBUSINESS RULESusing historical best practices. These rules are often situationally based,conditions to approve/deny use of data. Business rules are applied to enforce

Key Features

Quantifiable and interpretable privacy

On premises (and cloud ready)

Use all data without moving or modifying

Confidently generate statistics, aggregates and ML to support your AI initiatives

Supports data at any scale

On prem or cloud dashboard view

Solutions to maximize
your data value.

Different data projects have different considerations when it comes to privacy. We put you in control. LeapYear installs in your environment, on premises or in your cloud, and is configured for the specific data use and privacy challenges you face.

Solutions based on LeapYear are configured to ensure you capture maximum value and privacy.

Solution Features

FILTER
  • all
  • Capital Markets
  • Alternative Data
  • Sensitive Data Projects
Capital Markets

Works with all transactional data sets

Capital Markets

Protect client strategies

Capital Markets

Prevent alpha leakage

Capital Markets

Share insights across desks

Capital Markets

Bridge retail and institutional silos

Alternative Data

Never send data to anyone

Alternative Data

Protect individuals

Alternative Data

Protect entities

Alternative Data

Enable partners

Alternative Data

Retain control

Sensitive Data Projects

Flexible configuration for your projects

Sensitive Data Projects

Rigorous, deterministic privacy

Sensitive Data Projects

Compliance with the world's toughest data privacy laws

Sensitive Data Projects

Support Al/ML initiatives that need access to sensitive data

Sensitive Data Projects

Protect PII, PHI
 and entities simultaneously

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