Data encryption features for computer hardware and software profitability: I/O ports, expansion slots, algorithms, cyphers and security

Computers & Security, 10 (1991) 345-357

Data Encryption Features for Computer Hardware and Software Profitability: I/O Ports, Expansion Slots, Algorithms, Cyphers and Security Avi Rushinek’ and Sara Rushinek* ‘Accounting Department, University ofMiami, 317 Jenkins, Coral Gables, FL 33 124, U.S.A.

‘CIS Department, University ofMiami, 421 Jenkins, Coral Gables, FL 33 124, U.S.A.

The use of encryption to protect sensitive data has existed in one form or another throughout history. Today, the massive amounts of data transmitted regularly make it more important than ever to ensure the integrity of important information during transmission by means of data encryption. The selection of appropriate features for inclusion in data encryption products can be very frustrating for both hardware and software manufacturers, as well as for end-users. This study describes an interactive microcomputer-based feature selection system. The feature price contribution margin measures to what degree a specific feature meets end-user needs. It compares the outcome of integrating and bundling a feature into a product with marketing this feature as a separate “add-on” product line.

Kvords: Data encryption, Hardware and software profitability, I/O ports, Expansion slots, Algorithms, Ciphers, Security.

1. Introduction and Theoretical

Foundations

he T use of encryption to protect sensitive data has existed in one form or another through-

out history, from the ancient Romans sending coded messages to modern armed forces protecting

0167-4048/91/$3.50 0 1991, Elsevier Science Publishers Ltd.

A. Rushinek and S. RushineklData Encryption Features for Profitability

data transmissions [9]. Today, the massive amounts of data transmitted regularly make it more important than ever to ensure the integrity of important information during transmission by means of data encryption (DE).

Encryption consists of converting the data into an unintelligible form called a cipher, which can then be decrypted only by authorized parties. Data is encrypted using a mathematical algorithm controlled by a key [ 11. The longer the keyword or phrase, the more difficult it is to break the code. The key system is designed so that the data can be decrypted only by someone who has the appropriate key. A symmetrical cipher requires the encryption and decryption keys to be identical. An asymmetrical cipher, or public key system, allows separate keys for encryption and decryption so that the encryption key can be published [ 11.

The Data Encryption Standard (DES) of the National Institute of Standards and Technology is currently the accepted encryption standard for commercial data transmissions. Since its development at IBM in the early 197Os, the DES has remained the most widely used encryption method, although the National Security Agency has recently begun using its own system for classified data called the Commercial COMSEC Endorsement Program [8].

Selecting the most profitable features and deter- mining the prices of DE products can be a difficult process. Several factors are involved in this process including retail strategy, competition, current market price, expense and profit goals, consumer perceptions, psychological issues, leader offerings and environmental influences [6,7,2 11.

Research shows that product features also affect price Some studies have measured indirect price feature relations [5, lo]. However, the direct impact of individual features on price has not been empirically measured. This study focuses on the direct effects of DE features on the retail price, with the aid of segmented income statements.

2. Segment Reporting

Segment reporting shows the profitability of one portion of an organizational activity, frequently a product line. It measures product line profitability, promoting the most lucrative product. The usefulness of segment reporting is controversial. Some consider segment reporting to be valuable [4, 131. Others question the usefulness of segment reporting [ 15, 181. Many of these studies used multiple linear regression analysis. Likewise, this study also uses this analysis to be comparable with other studies.

This study extends segment reporting beyond the level of product line, breaking it down further into individual product line features. It advocates using feature segments prior to product line segment reports. Moreover, it segments contemplated future product lines or “add-on” product lines rather than currently existing product lines.

3. Theory of Feature Price Contribution Margin (FPCM)

This study contends that the most lucrative features should be built into a product. A feature-segmented income statement will point out these most profitable features. The complete product line segmented report should be considerd only after the most profitable features have been incorporated into the product. Otherwise, a traditional segmented product line report can be very mis- leading. One product may look more attractive only because more profitable features were included than in another one. This theory contends that each product feature has a price premium or discount value associated with it. This premium or discount is the FPCM feature. Features are characteristics of a product line. Some features are continuous and essential. In contrast, other features are binomial (0 or 1) and optional. The FPCM shows the increase or decrease in the price as a result of the presence or absence of an optional product feature. This study computes the FPCM using a

346

multiple regression model. Once the FPCM is known, its expenses can be deducted to show the feature contribution and segment margins.

Having this information, the most profitable features will be incorporated into the product. Such segmented reports will have truly comparable product lines, fulfilling their profitability potential.

It is important to discover which features contrib- ute the most to the price. It reflects the consumer/ end-user sentiments and it guides the product designer. If the product contains these high FPCM features, it should command the highest possible price. This study provides a methodology for selecting the most profitable features to include in a contemplated future product line or as a separate “add-on” product supplementing an existing product.

4. Data Encryption Selected Feature Review

Vendors have had a dilemma in knowing which features they should incorporate into their products [ 161. DE can be achieved using hardware or software systems. Appendix A lists a number of DE hardware and software packages [3, 11, 14,201.

The DES algorithm is a single-key system and uses a three-step process for encrypting data. First, DES permutes, or scrambles, the data. Next, it shifts the permuted data. Finally, the resulting data are com- bined with the key. In this way, an identical key must be used in addition to the DES algorithm to recover the original data [2]. The process of per- muting and shifting the data and combining them with a 56-piece key (actually 64 pieces, but only 56 are active) means that the number of possible combinations of data is too large to guess randomly. Random guessing is also made more difficult by combining the data with random, nonrepeating data. Additionally, the DES process ensures that the message cannot be decrypted using part of the key because every item of encrypted data depends on every other item of the data and the key. Thus,

Computers and Security, Vol. 10, No. 4

ultimate system security depends on proper management of the key [2].

An example of a public key cipher is the Rivest, Shamir and Adleman (RSA) cipher. This system has the advantage that the encryption key can be published without compromising data security. Unfortunately, public key systems require much longer keys than private key systems and thus tend to operate much more slowly [l].

The major vendors of DE have responded to user criticism by revising their systems, based on perceived needs. The problem is that perceptions are subjective and error prone. FPCM will bc a more objective measure of user satisfaction as well as profitability for that particular feature. In this way manufacturers can supplement their subjective perceived user needs by more objective FPCM and profitability information.

However, despite the importance of profitability in DE design, there has been little guidance to the contribution each feature specification has towards the price. How much should each function add to the total price? This is answered by an interaction feature selection system (FSS).

While designing a DE product or revising an existing product, the vendor should decide which features to include in the new release. Some of the DE features that should be considered include hardware, software, parallel port, expansion slot, loan onto hard disk, encrypts files, uses data encryption standard, certified by National Bureau of Standards, private key, public key, time required to encrypt 100 Kbyte, source file unencrypted, encryption automatic at save [16]. This is not an exhaustive list of DE features. It is just a sample of features intended to illustrate FPCM and to demonstrate FSS.

5. Data Collection, Processing and Hypotheses

Data are selected from the entire population of DE vendors listed. in the Data Sources manual [22]. Data were collected by inspecting the product

347


and its literature, verifying it and storing it in a database. Each vendor recorded in this database contains a DE product retail price and the specifi- cations of its features. The price is regressed on the features and the resulting X-coefficients are incorporated in an interactive FSS. The FSS is supplied with the feature specifkations, costs and sales volume data. This produces segmented financial statements.

The true hypothesis of this study is that FPCM

TABLE 1 Multiple regression summary table

Multiple rqpion summary rqmr

Kegrcssiou output

h-e-features base price

Base price error

Adjusted first Manufacturer’s population

F-value

Estirnatcd price contriburion

Estimated price contribution

error

Expansion Loan onto slot hard disk

- 132.14 56.84 135.17

192.62 183.88 233.59

- O.hY 0.31 0.58

0.08 0.7Y O.Y5

0.27 0.40 0.22

l’ime required SourceJle for encryption unencrypted

Private hblic

kry key

- 192.28 -81.22

118.1 1 1 1 1.70

- 1 .h3 - 0.73 0.04 0.92 0.14

0.79 0.33 30.79 0.36 0.38

0.40 0.47 4 1.70 0.48 0.49

exists, although R-square and F-values are used as its statistical surrogates. Retail prices are regressed on selected features of the population of all DE products. Therefore, only the “overall” goodness of fit of the regression equation is tested.

The overall test for goodness of fit is based on the value of the adjusted R-squared. Accordingly, the hypothesis concerning R can be stated formally as follows:

Null hypothesis: R = 0

Hardware Soffware l’arallel Purl

1

0.67

725.88

122.92

5.9 1 1.73 - 1.74

0.44 0.87 0.10

050 0.33 0.30

0.05 96.90 13.87

1.47 105.09 101.65

2

Constant Standard error of Y estimate

K-squared Number of observations

Degrees of freedom

X-coefficient(s)

Srandard error of coefficient

Statistical significance of 7

?-statistic

Average per feature

Standard deviation per

feature

MultipL regression summary repori

X-coefficient(s) Standard error of cocffcient Statistical significance of 7’

T-statistic

Average Standard deviation

Multiple ryesion summary report

X-coefficient(s)

Standard error of coefficient Sratistical significance of T

T-statistic

Average Standard deviation

*Significant at 0.05.

- 243.1’)

2 14.4’)

0.6’)

3Y.00

25.00’ 4.23

312.33

180.45

Uses data certijkd by rnrfyplion standard

196.65 - 161.73 145.90 126.76

1.35

0.62

0.49

3

- 362.20

208.58

- 1.28

0.41

0.49

Encrypts aufomatically

348


Alternative hypothesis: R # 0.

The features with the largest R-square contributions and t-values were selected from an extensive list of initial features.

6. Results, Discussion and Analysis

The price is regressed over the product feature spe- cifications. This is done by a multiple linear regression, since its assumptions have been met [12].

Since many past studies have used multiple regression, this study will use the technique for com- parability purposes.

Table 1 presents the summary statistics used for the overall test for goodness of fit of the regression model. Accordingly, the adjusted multiple R- square (0.67) indicates the variation in price explained by these FPCMs. The rest may be explained by other factors (Fig. 1) not included in this model. The standard error of the estimate shows the possible variation in the standard devia- tions, averages, T-values, and most importantly the X-coefficients that form the FPCM. The F-value (4.23) shows that the model is statistically significant at the 0.05 level. While the F-value pertains to the overall model, the T-value depicts the significance of individual coefficients (i.e. hardware is

highly significant at 5.91). The X-coefficients reflect the increment (or decrement) in the price due to the presence of a feature. Thus, if hardware capabilities are included, the price should be raised by $725.88.

The standard deviation is a measure of dispersion. It shows that there is greater dispersion about hardware (0.50) than about software (0.33).

The average shows the percentage of products in the population having a particular feature. Thus, hardware occurs less frequently (44%) than software (87%). U 1 k h b’ n i e t e momial features, whose values are either 0 or 1, the average of continuous features represents the industry standards. Thus, the time required to encrypt 100 Kbyte is about 30.79. A summary of these results is given graphically in Figs. l-4.

7. Interpreted Feature Price Contribution Margin Results

Based on the positive or negative values of the X- coefficients, the effects of the features upon the overall price can be classified as having tither expanding (positive) or contracting (negative) effects. The signs may reflect user perceptions, level of satisfaction and/or utility.

Fig. 1. Hardware/software composition of 47 data encryption

packages.

c

E-232 ONLY

(27 3%)

NONE (40 9%)

K-232 AND

PARALLEL (18.2%)

EXPANSION SLOT (13 6%)

Fig. 2. Hardware features of 22 data encryption packages with hardware capabilities.

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NONE (4.3%) FILES ONLY (10.6%)

FILE, DISK. AND DATA (38.3%) FILES AND DISKS ONLY (25.5%)

DATA ENCRYPTION ONLY (4.3%)

FILES AND DATA ENCRYPTION ONLY (17.0%)

Fig. 3. Filr, disk and data encryption capabilities of 47 packages.

6

0 UNDER $100 $lOO-$250 $250-9b500 $500-B 1000 OVER $1000

Fig. 4. Price ranges of 47 data encryption packages.

350

Computers and Security, Vol. IO, No. 4

The hardware (725.88) and software (312.33) features have expanding effects on the overall price, while the parallel port feature has a contracting effect. The negative coefficient of the parallel port feature may be explained by some negative associa- tion with this feature. In contrast, the hardware feature has a positive FPCM. In general, hardware encryption devices usually please users with their speed.

Add-on product vendors can use these coefficients to make decisions. Thus, the market reception of “add-on” hardware features may be more favorable

than the parallel port feature utilities. These are products that do not function alone. Instead, they are designed to supplement another product (usually by another vendor). Thus, a private key function utility will enable a user to lock and unlock data. However, it is important to emphasize that all these are “after-the-fact”, unconfirmed conjectures and rationales. Investigating and explaining each of thcsc coefficients is a future area of research in its own right and deserves its own theoretical and empirical testing. Thus, it is clearly beyond the scope of this study. Our concern is the general approach of interpreting these coefficients

TABLE 2 Calculation of estimated features price contributions

Illustratedfor adding additionalfeatures

Hardware sojware l’amllel Port

Binomial (optional) feature ranks

X-coefficient(s) multiplied by

Feature specs per product

Feature price contribution

margin (FPCM) product total

Constant (base price)

Total estimated price (see Table 3)

Without extra features

Add the first extra feature Estimated price with one extra

feature

Illustmtedforaddin~ additionalfeatures

Binomial (optional) feature

X-coefficient(s) multiplied

Feature specs per product

Feature price contribution

margin (FPCM) product total

Illustratedfir addiq additionalfeatures

Binomial (optional) feature X-coefficient(s) multiplied

Feature specs per product Feature price contribution margin (FPCM) product total

1 2 I2 725.88 312.33 - 362.20

I .oo I .oo 0.00 _--___-_____________--_________

$1,007.52 $725.88 $312.33 $0.00 ($243.10) q ================~===~==~===~==

$704.33 $725.88 (see Table 3, Column C)”

$1.490.21 (see Table 3, Column B)

Expansion Loan onto Encrypts Uses data Cer@ed 6y slot hard disk files encfyptiorl slaudnrd

9 6 4 3 10

- 132.14 56.84 135.17 1 Y5.65 - 161.73

1 .oo 1 .OO 1 .OO 0.00 0.00

($132.14) $56.84 $135.17 $0.00 $0.00

Privale Public Time required SortrceJze Erlctypts

kY keY J&encryption unenctypted autortraticnlly

11 8 h 5 7

- 192.28 -81.22 0.05 96.YO 13.87

1 .oo 0.00 90.00 1 .oo 0.00

($192.28) $0.00 $4.81 $YO.YO $0.00

‘The largest value for an optional feature (binomial spec 0 or I). bEssential features (continuous-other than 0 or 1) are not ranked

351


as they relate to the scgmentcd income statement model.

8. Statistical Interpretations

The study uses the X-cocffcients as FPCM, integrated into the cxpenscs and volume of sales, to measure fcaturc profitability rather than to evaluate user needs and standards. This conversion of FPCMs into dollars of a contcmplatcd future product line is included in Table 2. The estimated price is the sum of the FPCM values for all fcaturcs added to the constant.

Once a current estimated list price has been estab- lished, extra features can bc considered by adding their FPCM to the initial estimated list price (hard- wart, $725.88).

Table 2 shows the X-cocfficicnts from Table 1, and a ranking of the binomial features. It is interesting to note that DE hardware and the software features are the highest ranked favorites (ranked 1 and 2). In contrast, parallel port is the least favorite among end-users (ranked 13). Although interesting, this is not the focus of this study, but it may be used as a prominent criterion for add-on product dcvclop- ment consideration.

The focus of this study is converting the X- coefficients into FPCM, to cvaluatc the profitability of a feature or a group of features. This is done by multiplying the X-coefficients by the future spcci- fications of this fcaturc. For example, 90.00 s is specified as the time required to encrypt 100 K- byte, hardware is included (1), while encryption automatic at save is excluded (0), etc.

Table 2 shows how the estimated prices are computed. First, X-coefficients are multiplied (56.84, loan onto hard disk) by their specification (1) forming the FPCM total per feature end product ($56.84). Thcsc FPCM arc summed and added to the constant ( - 243.19) forming the price without an extra feature. Now, the question is how much should be charged for additional hardware

features! The charge is equal to the X-cocfflcient and FPCM ($725.88). WC then add this to the initial price to get the price for the product with one extra feature ($1490.2 1).

Table 3 shows how the FPCM for “hardware” is incorporated into the segmented income statement. It shows that FPCM ($725.88), its unit contribution margin ($542.49) and total contribution margin ($607 456) arc positive. It shows that adding the hardware feature will increase the volume and ulti- mately the segment margin ($587456). Therefore, the data show that it is favorable to add an extra feature. Howcvcr, another opportunity may be to produce an add-on product line that provides the hardware feature as a supplement to existing DE products. Such an add-on product line can be sold on its own. The opportunity for the add-on product line is presented in Table 4.

Table 4 shows that the add-on approach is more profitable than the integrated alternative ($110 7 17). This short-run scenario results from lower variable costs, substantially larger volume of sales, and larger total contribution margin ($868 173). The picture changes dramatically in the long run. In the long run (when frxcd costs are avoidable), the segment and not the contribution margin is the critical factor. It shows that option 3 ($6 18 708) is by far the most lucrative.

9. Limitations and Future Directions

There is an explicit assumption that certain product features arc desirable enough that a potential purchaser would pay more for a given encryption product. However, the list of characteristics in question (hardware, software, parallel port etc.) may be secondary discriminators. The critical issues in the selection of DE products may be their abso- lute effectiveness (i.e. whether they arc effective at protecting data in transmission), key management technology, and the reliability and reputation of the manufacturer. While the other features may be considered, they are mostly thought of as binary, rather than differential terms. For example, a pro-

352


TABLE 3 Product line/feature contribution and segment margins

Alpha Software and Computer Information Systems Inc.

Segmented income statement for the year ending December,

198X

By product lines and I extra feature added

Estimated unit orfeature data Estrajature added

Cohn A No (0) added

f eatures

Column B One (I) added

f rature

Column C (dl@rence (If columns A and B) Extrafeature dt@rence

Prices and FPCM from Table 2

Times variable cost rate3

Less variable cost in dollars

$764.33 $1.190.2 1 $725.88 - 15% - 2 0% - 51%

($1 14.65) ($298.04) ($183.30)

Unit/feature contribution margin

Times volume of units soldA

Total contribution margin

Less direct fixed expenses’

S64Y.68 $1,192.17 $542.4’) 000 I.000 100

$584,710 $1,192,160 $607,450 (150,000) (170,000) (20,000)

Segment margin

Common fixed cost 43‘47 10 1.022.166 587,456 (50,000) (50,000) 0

Net income $384,710 $972,166 $587,456

= q 1 q q

“Represents exogenous user entry for sensitivity analysis and evaluation. To prepare feature-scgmentcd statements it is necessary to

keep records of sales and variable expenses by features, as well as totals for the product. Having the information of sales and variable

expenses available, a contribution margin figure can be computed for each alternative product design (columns A - 1~). This largest

FPCM is $725.88.

duct may not bc bought at all if implemented in as other factors. In addition to linear relationships, software, not at all if it is not DES, etc. curvilinear relations should be explored.

In this study, the regression model is basically a price equation based on a variety of features. More- over, since this is a cross-sectional study, covering a single time period, it does not show which variable “causes” another, or whether both are the result of a third factor. However, the model does help in identifying the possibility of causation. The direc- tion of causation needs to be derived by multi- period, cross-section and time-series multiequarion analysis; controlled experimentation, and/or management judgement not only for prices and features but also for expenses and volumes as well

The formula for any given company should be double-checked against the formulae for its competitors, and against the same company’s prior and later data, to verify its reliability. Even within the same microcomputer industry, the formulae are expected to differ significantly among vertical markets and products, as well as years. Therefore, tailoring and updating are mandatory. It would also be important to investigate the multiplicative effects in addition to the additive effects, not only of cxpcnses and volume but also of incorporating other factors such as brand recognition, market

353


TABLE 4 New “add-on” product linc/fcaturc contribution and segment margins

Scpentcd income statcmcnt for the year ending

Dccembcr, I Y8X 13y potential “add-on” product lines each mc made

out of a product fcatarc

Add-on IKW product lines made out of

fcaturcs with the highest Fl’CM rankings

FPCM rank for optional fcaturcs 0 or I FPCM diffcrcncc bcwccn fcaturo

Fl’CM dcclinc rate = FPCM diffcrcncc/FPCM

Top rank4 FPCM from Table 2

Times variable cost rate

Less variable cost

Unit/fcaturc contribution margin

Times additional volume of units sold

Total contribution margin

LCQ direct t&cd expcnw

Scgmcnt margin for add-on product/fcaturc

Les scgmcnt margin from Table 3”

Hurdwm f&m

I

$725.88

- 20%

($147.10)

$578.78

I ,.iOO

$808,173

(170,000)

hY8.173

(.x7.150)

&$warc, jntrire

($113.5:)

- 5 7%

$3123.3

- 4%

($13.62)

$2Y8.7 1

2,250

$672,095

(YO.854)

$575,211

usises nntil encryption

($115.6:) - 37%

$1 Y6.65

- 1%

($2.70)

$ I Y-3.9.5 3,375

$054,580

(35,872)

$0 18,708

Net add-on advantage wcr intcgratcd option $I 10,717

,‘Figurc taken from Table 3, Column C, Net Income

Comparison bctwccn marketing it as a separate add-on product lint wrsus integrating it as an extra fcaturc in an misting product lint

Ihffcrcncc bctwccn Table 1, Column I and Table 3. Column C

The add-011 option IS more attractive by $552,013

becauw of reduced variable costs per unit ($3cr.2Y)

and incrcasc in volume ofralcs to conipctitor’s cubtonm~ 500.00

leader etc. In the microcomputer industry, this information may be difficult to obtain and veri@ for accuracy. The multiplicative effect should be added to the additive effect used in this study.

10. Summary, Conclusions and Implications

In summary, multiple regression has been used to measure the FPCM for several DE features. This

354

model can be used not only to value the degree of meeting end-user needs, but also to evaluate manufacturer’s profitability or alternative future DE designs.

This investigation concludes that FPCM cocffi- cients can be ranked and can reflect user desires. Hardware and software compatibility were ranked highest, while parallel port and private key were ranked lowest. Moreover, the integration of FPCM into the FSS has been demonstrated, producing comparative segmented income statements by future product line, by feature, and add-on products. The short- and long-run indicators have clearly been distinguished and illustrated. The intc- grated and add-on altcrnativcs have been com- pared and evaluated.

The implication of this study is that future segmented reports may incorporate the concept of FPCM, and describe future products and features rather than only existing whole product lines. This may raise the usefulness of segmented reports, reduce the controversy that surrounds them, and make them more widely spread.

Further studies are already under way to incorporate more DE features, and to investigate not only the additive FPCM but also the interaction and multiplicative effects among more features as well as combinations of other factors. Retail strategy, competition, consumer perceptions, psychological issues, leader offering, demand supply, and general time-series and cross-sectional economic trends are other factors that should all be eventually integrated into this model.

Finally, there is a presumption in the overall thesis that the market for DE products is price sensitive. This may prove to bc both true and untrue. For the bulk of the market, if encryption is not very cheap, features at any price cannot sell the product. For those in cspccially sensitive fields (i.e. the military, banking), effectiveness so outweighs any other characteristic that relative price may be hardly considcrcd.


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Appendix A: Product/Vendor List for U.S.A.

Blue: Lynx Data Security, Techland Systems Inc., 25 Waterside Plaza, NY, NY 10010, (212) 684

7781.

355


Appendix A: Product/Vendor List for U.S.A.

CIA 3.0, General Data Security Systems, 1127 Loma Ave., Long Beach, CA 90804, (213) 494- 1127.

Codctext, Digital Signal Equipment, Inc., P.O. Box 890584, Houston, TX 77289-0584, (713) 480- 7496.

Confidante, and Confide, Sccurc Systems Tcchnol- ogy Inc., 1966 E. 66th St., Cleveland, OH 44013, (800) 627-5580.

Crypt, Pica Publishing, 305 Second St., SE, 512 Paramount Building, Cedar Rapids, IA 52401, (3 19) 362-6964.

Cryptolock, MPPI Ltd., 2200 Lehigh Ave., Glen- view, IL 60025, (312) 998-8401.

Crypt Master/8, Crypt Mastcr/l6, and Crypt Master/24, Digital Signature, 5453 S. Woodlawn Ave., Chicago, IL 60615, (312) 324-6533.

CryptoGard, Advanced Computer Security Con- cepts, 4609 Logsdon Dr., Annandalc, VA 2203, (703) 354-0985.

Cryptolock, Commcrypt, Inc., 11005 Piney Meet- inghouse Rd., Rockville, MD 20854, (301) 299- 7337.

Cryptopath, Integrated Applications Inc., 8600 Harvard Ave., Cleveland, OH 44 105, (2 16) 34 l- 6700.

CYCOM, TLC, 1605 Main St., Suite 810, Sarasota, FL 33577, (800) 237-4433.

Cylock, Control Inc., 4620 W. 77th St., Edina, MI 55435, (612) 835-4884.

Data Encoder, IBM, P.O. Box 1328, Boca Raton, FL 33432, (800) 447-4700.

Data Padlock, Glcnco Engineering Inc., 3920 Ridge Ave., Arlington Heights, IL 60004, (312) 392- 2492.

Datasale, Trigram System, 3 Bayard Rd. # 66, Pittsburgh, PA 152 13, (412) 682-2 192.

DES 2000, Practical Peripherals, 3 1425 La Baya Dr., Wcstlake Village, CA 9 1362, (8 18) 99 l-8200.

DESMAUNS, Prime Factors Inc., 6529 Tele- graph Ave., Oakland, CA 94609, (415) 654-5090.

DES-PAC, Hawkeye Grafix, P.O. Box 1400, Olds- mar, FL 34677, (813) 855-5846.

Encryptor 304, Encryptor 305 and Encryptor 306,

Futurex Security Systems Inc., 9700 Fair Oaks, CA 95628, (916) 966-6836.

ESCRYPT/MS, Prime Factors Inc., 6529 Telegraph Ave., Oakland, CA 94609, (415) 654-5090.

File Encrypt, Wisdom Sofnvarc, Inc., P.O. Box 460310, San Francisco, CA 94146, (800) 456- 7276.

Guardisk, E-X-E Software Systems, 250 E. 17th St., Suite I, Costa Mesa, CA 92627, (7 14) 662-2535.

IRE SC3000, Information Resource Engineering Inc., 5024 Campbell Blvd., Baltimore, MD 2 1236, (30 1) 529-7500.

ISAC 3200, Isolation Systems Ltd., 14800 Quorum Dr., Dallas, TX 75240, (214) 404-0897.

iSECURE Cryptosystem, iMANAGE Software Co., P.O. Box 31151, Houston, TX 77231, (713) 721- 7100.

Key, Technical Communications Corp., 100 Domino Dr., Concord, MA 01742, (617) 862- 6035.

Mailsafe, RSA Data Security, Inc., 10 Twin Dolphin Dr., Redwood City, CA 94065, (415) 595-8782.

Micro Slave, Cryptext Corp., P.O. Box 425, Northgate Station, Seattle, WA 98125, (206) 775-6890.

M/PSYPHER, Prime Factors Inc., 6529 Telegraph Ave., Oakland, CA 94609, (415) 654-5090.

Multi-Function Encryptor and Remote Control, Secure Telecom, Inc., P.O. Box 70337, Sunny- vale, CA 94086, (408) 992-0572.

N-Code, K & L Software, 11425 Oak Leaf Dr., Silver Springs, MD 20901, (301) 681-8527.

N’cryptor, Mainstay, 5311-B Derry Ave., Agoura Hills, CA 9 130 1, (8 18) 991-6540.

PC Crypt, Glenco Engineering Inc., 3920 Ridge Ave., Arlington Heights, IL 60004, (312) 392- 2492.

PC Lock III, MPPI Ltd., 2200 Lehigh Ave., Glen- view, IL 60025, (312) 998-840 1.

P/C Privacy, MCTEL, Dept. 1022 Three Bala Plaza E., Suite 505, Bala Cynwyd, PA 19004, (2 15) 668-0983.

Phasorcode 1000, International Phasor Telecom Ltd., 134 Abbott St., Vancouver, BC, Canada V6B 2K6, (604) 683-7636.

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Privacy Plus, United Software Security, 6867 Elm St., Suite 100, McLean, VA 22101, (703) 556- 0007, or (800) 892-0007.

Secure Filer, Secure Filer Plus and Secure Filer Network, Cross Information Co., 1881 9th St., Suite 311, Boulder, CO 80302-515 1, (303) 444- 7799.

Secureware Jr., Secureware I, Secureware II and Secureware III, Remote Systems Inc., 38 Cessna Ct., Gaithersburg, MD 20879, (301) 869-6500.

SecuriKey, Micro Security Systems, Inc., 4750 Wiley Post Way, Suite 180, Salt Lake City, UT 84116, (800) 456-2587.

Security, The Answer is Computers, 6035 Univer- sity Ave., Suite 7, San Diego, CA 92115, (619) 287-0795.

Security Module, Prime Factors Inc., 6529 Tele- graph Ave., Oakland, CA 94609, (415) 654-5090.

Sentinel, SuperMac Technology, 485 Potrero Ave., Sunnyvale, CA 94086, (408) 245-2022.

Softguard, Advanced Computer Concepts, 4609

Logsdon Dr., Suite 439, Annandale, VA 22003, (703) 354-0985.

Softpro 100, Softpro 200, and Softpro 500, Voyager Development Inc., 412 S. Lyon St., Santa Ana, CA 92701, (714) 667-8128.

Stoplock IIR, Q uest IV Industries, 901 Kentucky, Suite 304, Lawrence, KA 66044, (913) 842-78 15.

SuperKey, Borland International, Inc., 1800 Green

Hills Rd., P.O. Box 660001, Scotts Valley, CA 95066, (408) 438-8400.

Systemate, Systemate Inc., 1106 Clayton Ln., Suite 200E, Austin, TX 78723, (5 12) 458-6202.

Tango, and TED 1027, Teneron Corp., 6700 S.W. 105th Ave., Suite 200, Beaverton, OR 97005, (503) 546-l 599.

Transcryptor, Cryptext Corp., P.O. Box 425, Northgate Station, Seattle, WA 98125, (206) 775-6890.

WHISPER (DES), Milka L.P., 1000 Holcomb

Woods Parkway, Roswell, GA 30076, (404) 993- 4421.

Avi Rushinek is an Associate Professor

of Accounting and Information Systems

at the University of Miami. He holds a

Ph.D. from the University of Texas at

Austin. His interests include accounting

information systems, managerial/cost

accounting, EDP auditing, and business

applications for mainframe, mini and

micro computers.

Sara Rushinek is currently an Asso-

ciate Professor of Computer Informa-

tion Systems in the Department of

Management Science at the University

of Miami. She received her Ph.D. from

the University of Texas at Austin. Her current interests are in the area of com-

puter-assisted instruction, computerized

management information systems, data-

base management systems, program-

ming languages, research methods and statistics.

357

Documents

Data encryption features for computer hardware and software profitability: I/O ports, expansion slots, algorithms, cyphers and security